1. Pneumatic Injection Mould Machine Capability and
Techno-Economic Study
Matthew Paul Keyser
Department of Industrial Engineering
University of Stellenbosch
Study Leader: Theuns Dirkse van Schalkwyk
Final year project presented in partial fulfilment of the requirements for the degree of
Industrial Engineering at Stellenbosch University
B.Eng Industrial

2. i
Declaration
I, the undersigned, hereby declare that the work contained in this final year project is my own original
work and that I have not previously in its entirety or in part submitted it at any university for a degree.
…………………………… ………………………
Signature Date

3. ii
ECSA Exit Level Outcomes Reference
Exit level outcome Section(s) Page(s)
1. Problem solving All All
5. Engineering methods, skills & tools, incl. IT
4, 5 & 6 31 - 55
6. Professional & Technical communication
All All
9. Independent learning ability
2 & 3 5 - 30
10. Engineering professionalism
All All

4. iii
Abstract
The injection moulding process constitutes a vital part of the manufacturing sector and plastic
injection moulding (PIM) is one aspect of this process. The following report discusses the
characteristics and capabilities of a custom made injection moulding machine (CIMM) powered by
pneumatics, with the purpose of moulding plastic parts. These moulded parts will be measured and
analysed statistically in order to determine the optimal operational settings for the CIMM.
For the optimal settings to be determined, a set of experiments needs to be executed. Various
literature was studied to ensure an appropriate project methodology would be implemented to
successfully carry out the experimentation. Consequently, it was determined that a full factorial
design of experiments (DoE) would be executed with the assistance of Taguchi’s Optimisation
Method and various statistical analysis. Twenty-seven experiments were executed with each
experiment consisting of a unique combination of three factors, at three different levels. The three
factors are as follows:
1. The temperature at which the plastic is moulded (MT), in degrees Celsius (o
C).
2. The length of time given for the plastic to fill the mould (FT), in seconds (sec).
3. The length of time kept in place before ejecting part (PT), in seconds (sec).
Once all twenty-seven experiments had been completed, each moulded part was inspected and
measured for data collection according to the following four criteria:
1. Visual inspection of part conformance
2. Rework time, measured in seconds (sec)
 Rework time for runner
 Rework time for finishing
3. Part weight, measured in grams (g)
4. Part thickness, measured in millimetres (mm)

5. iv
The above analysis provided the data that would be used to determine the optimal operational setting
for the CIMM by conducting the following forms of analysis:
 Initial observation analysis
 Techno-economic analysis
 Statistical analysis
 Descriptive statistical analysis
 ANOVA analysis
 Process control analysis
 Waste Analysis
The statistical analysis was executed with the assistance of RSudio which is the interface for the open
source statistical software package R. All forms of analysis shared the same two objectives; total cost
per part (𝑇𝐶 𝑝𝑎𝑟𝑡) must be minimised and part thickness must be maximised whilst minimising the
variation of part thickness in moulding process. The brief summary of the three possible operational
settings, based on their performance in the analysis process, are given in the table below.
A break-even analysis was then conducted to determine the single most optimal solution for the
CIMM so that a place for the machine in the business sector can be motivated. This resulted in
Experiment 14 being suggested as the most optimal solution due to its superior monthly profit which
will be the most beneficial factor moving forward, from a business perspective.
The findings of this project recommend that the CIMM would be best suited for small scale
production. This finding can lend itself to small business owners or enthusiasts as it is a compact and
mobile machine that can be operated and stored in a garage.
MT FT PT
14 190 10 5 1,26
23 200 10 5 1,26
26 200 15 5 1,67
Experiment TCpart (R )
Operational settings
Table 1: Three possible optimal operational settings for the CIMM.

6. v
Opsomming
Die spuitgietings-proses speel 'n belangrike rol in die vervaardigingsektor en plastiese spuitvorms
(‘PIM’) is een aspek van hierdie proses. Hierdie dokument bespreek die kenmerke en vermoëns van 'n
doelvervaardigde spuitvormmasjien (‘CIMM’) wat pneumaties aangedryf word deur met die doel om
plastiese onderdele te giet. Hierdie gevormde onderdele sal gemeet en statisties geanaliseer word om
die optimale operasionele verstellings vir die CIMM te bepaal.
Om die optimale verstellings te bepaal, moet 'n stel eksperimente uitgevoer word. Verskeie bronne
uit die literatuur is bestudeer om ‘n toepaslike projek metodologie te identifiseer en te implimenteer
sodat die eksperiment suksesvol uitgevoer kan word. Gevolglik is vasgestel dat 'n volle faktoriale
ontwerp van eksperimente (DoE) uitgevoer sal word met behulp van Taguchi se Optimalisering-
metode asook verskeie statistiese analises. Sewe en twintig eksperimente is uitgevoer waar elke
eksperiment bestaan uit 'n unieke kombinasie van drie faktore, op drie verskillende vlakke. Die drie
faktore is soos volg:
1. Die temperatuur waarteen die plastiek gegiet is (‘MT’), in grade Celsius (o
C).
2. Die tydsduur om die gietvorm met plastiek te vul (‘FT’), in sekondes (sek).
3. Die tydsduur wat die onderdeel in die gietvorm in plek gehou word voordat die onderdeel
uitgeset word (‘PT’), in sekondes (sek).
Nadat al sewe en twintig eksperimente afgehandel is, is elke gevormde deel geïnspekteer en gemeet
vir die versameling van data volgens die volgende vier kriteria:
1. Visuele inspeksie om te bepaal of die onderdeel aan die vooraf bepaalde part vereistes
voldoen
2. Herwerk tyd gemeet in sekondes (sek)
 Herwerk-tyd vir die ‘loper’
 Herwerk-tyd vir die afwerking
3. Die onderdeel se gewig, gemeet in gram (g)
4. Die onderdeel se dikte, gemeet in millimeters (mm)

7. vi
Die bogenoemde ontleding het die nodige data verskaf om die optimale operassionele verstellings vir
die CIMM vas te stel deur die volgende vorms van analise te onderneem:
 Aanvanklike waarnemings analise
 Tegno-ekonomiese analise
 Statistiese analise
 Beskrywende statistiese analise
 ANOVA analise
 Proses beheer ontleding
 Verkwisting (afval) analise
Die statistiese analise is uitgevoer met die hulp van RSudio wat die koppelvlak is vir die “open
source” statistiese sagteware pakket R. Alle vorms van analise het dieselfde twee doelwitte gedeel;
totale koste per onderdeel (‘𝑇𝐶 𝑝𝑎𝑟𝑡’) moet tot die minimum beperk word en die onderdeel dikte moet
gemaksimeer word terwyl die variasie van die onderdeel dikte in die gietproses beperk moet word.
Die kort opsomming van die drie moontlike operasionele verstellings, soos gebasseer op hul prestasie
in die analise proses, word in die onderstaande tabel aangebring.
A gelykbreek analise gevolglik uitgevoer om die optimale oplossing vir die CIMM te bepaal sodat 'n
plek vir die masjien in die sakesektor gemotiveer kan word. Daarvolgens word Eksperiment 14
aanbeveel as die optimale oplossing weens sy uitstekende maandelikse wins. Vanuit ‘n
besigheidsperspektief is hierdie wins die mees voordelige faktor om vooruit te gaan.
Die bevindinge van hierdie projek beveel aan dat die CIMM mees geskik sal wees vir kleinskaalse
produksie. Hierdie bevinding leen homself tot kleinsake eienaars of entoesiaste, aangesien dit 'n
kompakte en mobiele masjien is wat in ‘n motorhuis gebruik en gestoor kan word.
Table 2: Drie moontlike optimale operasionele vertsellings vir die CIMM.
MT FT PT
14 190 10 5 1,26
23 200 10 5 1,26
26 200 15 5 1,67
Eksperiment
Operasionele verstellings
TCpart (R )

8. vii
Acknowledgements
I would firstly like to thank my family for the love and support they have shown me over my years at
university. Special mention has to go to my parents Min and Randolf Keyser. If it were not for all the
sacrifices they have made for me, I would not have had the privilege of studying Industrial
Engineering at an amazing university.
I would like to thank my good friend Soren Bruce for his time and effort in helping me with the data
collection which was a time consuming and tedious process due to the nature of this project.
I would like to thank my study leader, Theuns Dirkse van Schalkwyk, for his for his guidance and
assistance in the execution of this project.
Lastly, I would like to acknowledge my friends, especially my classmates, who have made the
engineering course just a bit more fun and enjoyable.
Jeremiah 29:11
“I know the plans I have for you,” declares the Lord, “plans to prosper you and not to harm you,
plans to give you hope and a future.”

9. viii
Contents
Declaration...............................................................................................................................................i
ECSA Exit Level Outcomes Reference .................................................................................................. ii
Abstract.................................................................................................................................................. iii
Opsomming............................................................................................................................................. v
Acknowledgements............................................................................................................................... vii
Contents ............................................................................................................................................... viii
List of Figures........................................................................................................................................ xi
List of Tables ........................................................................................................................................ xii
Glossary ............................................................................................................................................... xiii
1. Introduction.........................................................................................................................................1
1.1 Project Background and Information............................................................................................1
1.2 Problem Statement........................................................................................................................2
1.3 Project Aim...................................................................................................................................2
1.4 Project Objectives.........................................................................................................................3
1.5 Limitations and Assumptions of the Study...................................................................................3
1.6 Proposed Study Approach and Methodology ...............................................................................3
1.7 Structure of the Report..................................................................................................................4
1.8 Conclusion ....................................................................................................................................4
2. Literature Review................................................................................................................................5
2.1 Injection Moulding........................................................................................................................5
2.1.1 Background................................................................................................................................5
2.1.2 Pneumatic Injection Moulding...................................................................................................5
2.1.3 The Basic Process ......................................................................................................................6
2.1.4 Importance of Moulding Quality ...............................................................................................7
2.1.4.1 Moulding Materials.............................................................................................................7
2.1.4.2 Mould Requirements...........................................................................................................8
2.1.4.3 Mould Performance.............................................................................................................8
2.1.4.4 Accuracy and Finish............................................................................................................9
2.2 Design of Experiments................................................................................................................10
2.3 Taguchi Optimization Method....................................................................................................11
2.4 RStudio .......................................................................................................................................14
2.4.1 R as a Software Development Platform...................................................................................14
2.4.2 Design of Experiments in R.....................................................................................................15

10. ix
2.5 Techno-economic Study .............................................................................................................16
2.5.1 Cost per part.............................................................................................................................16
2.5.2 Break-even Analysis ................................................................................................................18
3. Project Methodology.........................................................................................................................19
3.1 Experimental Procedure..............................................................................................................19
3.1.1 Quality Aspect to be Optimised...............................................................................................19
3.1.2 Identify the Noise Factors and Test Conditions.......................................................................19
3.1.3 Identify the Control Factors and their Alternative Levels........................................................19
3.1.4 Design Experimental Matrix....................................................................................................20
3.1.5 Conduct Matrix Experiment.....................................................................................................21
3.2 Data Collection and Analysis Methodology ...............................................................................25
3.2.1 Quality Inspection Criteria.......................................................................................................25
3.2.2 Quantitative Analysis of Part Quality ......................................................................................25
4. Analysis of Data................................................................................................................................31
4.1 Initial Observation Analysis........................................................................................................31
4.2 Techno-economic Analysis.........................................................................................................33
4.3 Statistical Analysis......................................................................................................................34
4.3.1 Descriptive Statistical Analysis ...............................................................................................34
4.3.2 ANOVA Analysis ....................................................................................................................39
4.3.3 Process Control Analysis .........................................................................................................45
4.4 Waste Analysis............................................................................................................................48
5. Discussion of Results........................................................................................................................51
5.1 Summary of Results....................................................................................................................51
5.1 Break-even Analysis ...................................................................................................................52
6. Conclusion ........................................................................................................................................54
6.1 Suggested Operational Settings ..................................................................................................54
6.2 Improvements and Recommendations for Future Studies ..........................................................54
6.3 Skills Applied and Developed by Student ..................................................................................55
6.4 Benefits to Society......................................................................................................................55
7. References.........................................................................................................................................56
Appendix A...........................................................................................................................................58
CIMM Specifications........................................................................................................................58
Appendix B...........................................................................................................................................59
Classification of Design of Experiments ..........................................................................................59
Appendix C...........................................................................................................................................62
Factors for Control Charts ................................................................................................................62

11. x
Appendix D...........................................................................................................................................63
Coding used in RStudio ....................................................................................................................63
Appendix E ...........................................................................................................................................65
Digital Watt Meter ............................................................................................................................65
Appendix F............................................................................................................................................66
Planned Project Timeline..................................................................................................................66

12. xi
List of Figures
Figure 1: Image of the CIMM ..................................................................................................................6
Figure 2: Cyclic Sequence in the PIM Process (Rosato, 2000) ................................................................7
Figure 3: Polypropylene granules. ..........................................................................................................8
Figure 4: Mould for the part that will be manufactured for this project ...............................................9
Figure 5: Taguchi method flow chart (Unal & Dean, 1991). .................................................................12
Figure 6: Orthogonal Array (OA) Based Simulation Algorithm (Unal & Dean, 1991). ..........................13
Figure 7: Flow chart of the experimental procedure............................................................................23
Figure 8: The sequential numbering chart............................................................................................24
Figure 9: A visually conforming part, prior to rework...........................................................................26
Figure 10: A visually non-conforming part............................................................................................26
Figure 11: Conforming part following rework for runner.....................................................................27
Figure 12: Conforming part following rework for finishing. .................................................................27
Figure 13: Weighing part prior to rework (PW)....................................................................................28
Figure 14: Weighing part after rework (PWA)......................................................................................28
Figure 15: Illustrating where and how the part thickness was measured using the digital vernier
calliper...................................................................................................................................................29
Figure 16: A bar graph showing the number of conforming parts produced per experiment.............32
Figure 17: Removing moulded parts by hand.......................................................................................32
Figure 18: Histogram showing the distribution of total cost per part..................................................34
Figure 19: Statistical summary of measurable parameters and collected data. ..................................35
Figure 20: Total cost per part as a function of mould temperature.....................................................35
Figure 21: Total cost per part as a function of filling time....................................................................36
Figure 22: Total cost per part as a function of packing time. ...............................................................36
Figure 23: Thickness as a function of mould temperature...................................................................37
Figure 24: Thickness as a function of filling time..................................................................................38
Figure 25: Thickness as a function of packing time. .............................................................................38
Figure 26: Total cost as a function of mould temperature with linear trend line. ...............................39
Figure 27: Total cost as a function of filling time with linear trend line...............................................40
Figure 28: Total cost as a function of packing time with linear trend line. ..........................................40
Figure 29: Summary of ANOVA test between total cost per part and mould temperature.................41
Figure 30: Part thickness as a function of mould temperature with linear trend line. ........................42
Figure 31: Part thickness as a function of filling time with linear trend line........................................42
Figure 32: Part thickness as a function of packing time with linear trend line.....................................43
Figure 33: 3D scatter plot showing total cost per part as a function of filling time and mould
temperature..........................................................................................................................................44
Figure 34: 3D scatter plot showing part thickness as a function of filling time and mould temperature
..............................................................................................................................................................44
Figure 35: X-bar chart created in Excel.................................................................................................47
Figure 36: Summary of waste analysis..................................................................................................49
Figure 37: Appendix A - Specifications of the CIMM made by Lindmann Machines & Equipment
(Super Products Website, 2015)...........................................................................................................58
Figure 38: Digital watt meter that was used to measure energy consumption (kWh). .......................65

13. xii
List of Tables
Table 1: Three possible optimal operational settings for the CIMM..................................................... iv
Table 2: Drie moontlike optimale operasionele vertsellings vir die CIMM. .......................................... vi
Table 3: Advantages and disadvantages of PIM (Rosato, 2000).............................................................7
Table 4: Requisites and Tools for Sound Experimentation (Juran & Godfrey, 1998). ..........................10
Table 5: Advantages and disadvantages of R (Williams, 2012). ...........................................................15
Table 6: Control factors and their levels for experimentation. ............................................................20
Table 7: The L27 Orthogonal Array (OA)...............................................................................................21
Table 8: OA with Control Factors and their different levels for Experimentation................................22
Table 9: Table showing how the standard rework times were determined.........................................27
Table 10: The data collected for experiment 8 is shown here as an example of the data collected for
each experiment. ..................................................................................................................................30
Table 11: Unit costs for the measured cost parameters. .....................................................................33
Table 12: Summary of techno-economic analysis. ...............................................................................33
Table 13: Optimal based on the variation of total cost per part. .........................................................37
Table 14: Optimal settings based on the variation of part thickness. ..................................................39
Table 15: ANOVA results for total cost per part. ..................................................................................41
Table 16: ANOVA results for part thickness..........................................................................................43
Table 17: Optimal settings for total cost per part. ...............................................................................45
Table 18: Optimal settings for part thickness.......................................................................................45
Table 19: Summary of values used for the X-bar chart. .......................................................................46
Table 20: Summary of results for process control analysis. .................................................................48
Table 21: Experiments that are statistically in control. ........................................................................48
Table 22: Table showing wastage as a percentage of the material used for the completed part (i.e.
after rework).........................................................................................................................................50
Table 23: Summary of possible operational settings based on the conducted analyses.....................51
Table 24: Remaining possible optimal operational settings.................................................................51
Table 25: Summary of break-even analysis. .........................................................................................52
Table 26: Optimal operational settings for the CIMM..........................................................................54
Table 27: Appendix B.1 - Classification of Designs (Juran & Godfrey, 1998)........................................59
Table 28: Appendix B.2 - Classification of Designs (Continued). ..........................................................60
Table 29: Appendix B.3 - Classification of Designs (Continued) ...........................................................61
Table 30: Appendix C - Table showing factors for the different control charts (Evans & Lindsay, 2014).
..............................................................................................................................................................62
Table 31: Project timeline plan.............................................................................................................66

14. xiii
Glossary
Acronyms
ANOVA Analysis of variance
BEP Break-even point
CIMM Custom injection moulding machine
CL Center Line ( 𝑥̿)
DoE Design of experiments
FT Filling time (sec)
LCL Lower control limit
MT Mould temperature (o
C)
OA Orthogonal array
PIM Plastic injection moulding
PP Polypropylene
PT Packing time (sec)
PW Part weight (g)
PWA Part weight after rework (g)
SP Selling price per part (R)
UCL Upper control limit
VC Variable cost per part, in this project see 𝑇𝐶 𝑝𝑎𝑟𝑡
Symbols
𝐶 𝐸,𝑝𝑎𝑟𝑡 Energy cost per part (R)
𝐶 𝐸,𝑇𝑜𝑡 Total energy cost (R)
𝐶 𝑘𝑊ℎ Cost per kilowatt hour (R/kWh)
𝐶𝐿 Labour cost per hour (R/hr)
𝐶𝐿,𝑝𝑎𝑟𝑡 Labour cost per part (R)
𝐶𝐿,𝑇𝑜𝑡 Total labour cost (R)
𝐶 𝑀,𝑝𝑎𝑟𝑡 Material cost per part (R)
𝐶 𝑀,𝑇𝑜𝑡 Total material cost (R)

15. xiv
𝐶 𝑅𝑀 Raw material cost (R/kg)
𝐸 𝑇𝑜𝑡 Total energy consumption (kWh)
𝑛 Number of conforming parts per experimental run
𝑠 Standard deviation
𝑠̅ Average standard deviation
𝑇𝐶 Cycle time per part (sec)
𝑇𝑅 Rework time per part (sec)
𝑇𝑅,𝑓𝑖𝑛𝑖𝑠ℎ Rework time for finishing (sec)
𝑇𝑅,𝑟𝑢𝑛𝑛𝑒𝑟 Rework time for runner (sec)
𝑇𝐶 𝑝𝑎𝑟𝑡 Total cost per part (R)
𝑥̅ Mean
𝑥̿ Overall mean

16. 1
1. Introduction
The first chapter will introduce the project that will be undertaken and the problem that must be
solved. This will be achieved by providing some background on the problem as well as the objectives
that need to be met, along with the methodology that will also be implemented.
1.1 Project Background and Information
The machine that will form the centre of this project is a custom injection moulding machine or more
simply known as a CIMM. It was designed and built by Lindmann Machines and Equipment, a South
African company based in Cape Town. This company produced the CIMM with the purpose of it to
be ideal for entry level entrepreneurs wanting to execute small scale production. Consequently it was
also designed to be simple to operate and maintain.
The CIMM is a pneumatic type injection moulder and this means it uses compressed air to drive the
moulding process. Essentially, the mould is opened and closed using pneumatics and the material that
fills the mould itself is also driven into the mould using pneumatics. This is different from
conventional injection moulders which typically use hydraulics or electricity to power the systems
that create the final mould. An added benefit of a pneumatic machine is that it automatically releases
the final moulded part which is indirectly achieved with pneumatics. For the purpose of this study,
plastic will be the material used in the moulding process and will thus form the structure of the parts
that are produced.
The injection moulding machine has a built in programmable logic controller (PLC) which is a digital
computer, and this is where the input parameters are entered into to. There are four parameters that
can be programmed into the CIMM on this PLC and they include:
1. Temperature at which the heating element turns off (T1).
2. Temperature at which the machine injects molten plastic into the mould (T2).
3. The total length of time that the machine injects molten plastic into the mould.
4. The length of time that the mould is held closed before ejecting the part.
Both of these temperature settings are interlinked as the PLC has a two-way controller that aims to
keep the temperature as constant as possible during the moulding process. This two-way controller is
the reason for the two temperature setting parameters, however, because they are interlinked they will
be presented as one parameter for the remainder of the report, namely mould temperature (MT).
It must also be mentioned that parameter 3 will be presented as the filling time for the mould (FT) and
that parameter 4 will be presented as the packing time for the mould (PT). The packing time

17. 2
represents the time the mould is help in place to allow the mould to cool before it is ejected, following
the completion of the filling.
The variation of these three parameters will directly affect the quality of the finished parts as well the
time that it takes for each product to be produced. Since the aim of this project is to manufacture
products in the most efficient way while minimising the costs, the combination of these parameters
will prove imperative to the final results.
A machine is not able to perfectly replicate each part or product that it manufactures, due to natural
variations in the manufacturing process, and this is why tolerances exist. As long as the produced
parts conform to the specified tolerances, they will pass the quality inspections. The CIMM itself will
also produce parts that diverge from the specified tolerance levels as a result of these variations.
1.2 Problem Statement
It is unknown what combination of parameter settings on the CIMM will yield an optimal
performance. These parameters, outlined in the previous section, will have a direct effect on the
degree to which produced parts will conform to the tolerances. The parameters are formalised as:
1. The temperature of the molten plastic (MT).
2. The time allocated for the molten plastic to flow into the mould (FT).
3. The time allocated for the mould to be kept in place before the part is ejected (PT).
A combination of these three parameters must be determined in order to find the optimal operational
performance for the CIMM.
1.3 Project Aim
The purpose of this study is to identify the optimal economic value of the CIMM by determining the
optimal operational parameters of the CIMM to produce conforming parts and taking into account the
associated costs to manufacture these parts.
This will be achieved by statistically determining the optimal operational settings of the injection
moulder to produce these parts as efficiently as possible. Finally the production of parts has to be
accomplished as economically as possible in order to motivate a place for the machine in the business
sector.

18. 3
1.4 Project Objectives
The objectives of this project will be satisfied by conducting the following research steps:
 Experimentally determine the capabilities of the custom injection moulding machine.
 Examining the characteristics of the moulded parts by measuring and inspecting the final
product.
 Analysing the results using R statistics software.
 Based on findings determine the optimal operating settings of the CIMM to produce the given
parts.
 Complete a techno-economic study.
 Make recommendations based on the analysis.
 Motivate a business case for where the machine might be operated profitably.
1.5 Limitations and Assumptions of the Study
The study will acknowledge the following initial limitations:
 A pneumatic-type injection moulding machine will be the only injection moulding machine
type to be used in this study.
 Plastic will comprise the only material used for the moulding of the parts.
 Only one design part will be tested.
The study will then also acknowledge the following assumptions:
 Functions that are not available in R can be coded by the student.
 All software required to conduct the study will be readily available.
 A sufficient pneumatic-type injection moulder will be readily available to the student.
1.6 Proposed Study Approach and Methodology
A well thought and structured methodology needs to be put in place to insure that the objectives on
the problem are satisfied. Firstly, a literature study will be performed in order to gain a thorough
understanding of the problem at hand. This will include understanding the concept of injection
moulding and the fundamental properties of how it works. Additionally, it will look at the various
types of injection moulding and the different parts that are produced as a result of these variations.
The specific injection mould machine in question will also be analysed and assessed in order to
understand its functionality and how it should perform.
Parts will then be produced by this machine over a range of operational settings which will yield
significant data that will be documented. Firstly the quality of the finished parts will be assessed by

19. 4
measuring and inspecting that they meet the design parameters and secondly the optimal operational
settings for the injection mould machine will also be determined. This will be determined by
recording all the results and analysing all the data using R software which is widely used for statistical
computing and graphics.
In addition to the optimal operational aspect of the injection mould machine, the economic aspect also
has to analysed. This will be achieved by developing a techno-economic assessment. From an
economic perspective it will be important to produce each part as inexpensively as possible and this
will be most effectively reached by using as little material as possible.
The goal will be to manufacture high quality parts in the quickest possible time while still trying to
manufacture each part in the most cost effective manner. This will provide a good case to motivate a
position for this injection moulding machine in the manufacturing industry.
1.7 Structure of the Report
Chapter 2 covers the literature review phase of the project which forms a large portion of the final
year project and aided with understanding the tools that will be required to satisfy the project
objectives. Chapter 3 addresses the methodologies that were used to conduct the experiments and
capture the necessary data. In Chapter 4, the collected data was compiled and statistically analysed,
and in addition to this a techno-economic analysis was also conducted. The results of the project are
investigated and discussed in Chapter 5 in order to determine the optimal operating regime for the
CIMM. The final chapter, Chapter 6, constitutes the conclusion for the final year project which
provides a solution to the Problem Statement (Section 1.2) by satisfying the Project Aim (Section
1.3).
1.8 Conclusion
This chapter introduced the final year project by identifying the Problem Statement along with the
purpose of the project, shown in the Project Aim section. In addition to this, the objectives were laid
out with the proposed methodology illustrating how these objectives will be reached. Chapter 2 will
focus on the literature study which forms a vital step in meeting the objectives of the final year
project.

20. 5
2. Literature Review
This chapter will look at various areas of literature that deal with background information, research
and existing methods that relate to this research problem. The information will then be applied to the
project in order to successfully reach the outlined objectives.
2.1 Injection Moulding
Injection moulding is a significant a part of manufacturing. As the injection moulding industry has
evolved over the years so to have the machines that produce the final products.
2.1.1 Background
Nowadays there are a range of injection moulding machines, and this is because various models are
better equipped to manufacture specific parts over others, depending on the way that they operate. In
addition to different machine types there are also different materials that are involved in the injection
moulding process (Gauthier, 1995). The most common ones include plastic, metal and glass and for
this project only plastic will be analysed and this is termed Plastic Injection Moulding (PIM).
PIM is the most common manufacturing method for producing parts made out of plastic material. It is
an extremely versatile process that can produce parts with holes, springs, threads, hinges and
undercuts in a single operation (Gauthier, 1995). Moulded parts can be simple or complex and can be
solid, foamed, reinforced or filled. They can be small or large, thick or thin, flexible or rigid. Injection
moulded parts also lend themselves to endless decorative effects; they can be polished, textured, hot-
stamped, plated, coloured or clear (Gauthier, 1995). No other manufacturing process offers the range
of capabilities that injection moulding provides and this is what makes it such an appealing process.
Typical injection mouldings (moulded parts) can be found everywhere in daily life. Examples include
automotive parts, household articles, consumer electronic components and toys (Zhou, 2013).
In today’s manufacturing industry, there are four different types of injection moulding machines;
hydraulic, pneumatic, electric and hybrid (Thiriez & Gutowski, 2006). The classification is based on
the method of how each machine produces a part and this specifically looks at the driving system they
use. These different types of injection moulding machines range in size and complexity; from desk-
size units up to machines the size of a small house (Thiriez & Gutowski, 2006).
A custom pneumatic type injection moulding machine will be utilised for the purposes of this project.
2.1.2 Pneumatic Injection Moulding
Pneumatically operated injection moulding machines use compressed air to drive a plunger in the
injection moulding process. This makes them cheaper to run than the other types of injection

21. 6
moulding machines (Shukla, 2013). By having less mechanical parts it also reduces the chance of
mechanical failure and additionally there are no problems with oil leakage and fire hazards.
Figure 1 below shows the pneumatic type CIMM that will be used to conduct this research project.
The CIMM was designed and built by Lindmann Machines and Equipment who have stated on their
website that the machine “suits industry for short runs and prototypes and educational institutions as
teaching instruments” (Super Products Website, 2015). The exact design specifications of the CIMM
can be found in Appendix A.
2.1.3 The Basic Process
PIM is basically a repetitive and cyclical process in which melted plastic at high pressure is injected
into a mould cavity, cooled and held under pressure until it can be ejected in a solid state, duplicating
the shape of the mould cavity. The mould may consist of a single cavity or a number of similar or
dissimilar cavities, each connected to flow channels, or runners, which direct the flow of the melted
plastic to the individual cavities (Rosato et al, 2000).
Figure 2, on the following page, shows the basic sequence of operations which occur in a moulding
cycle: (a) heating and injecting, (b) moulding, and (c) ejecting.
Figure 1: Image of the CIMM

22. 7
To overview the benefits of the PIM, Table 2.1 presents the advantages and disadvantages of the PIM
enterprise (Rosato, 2000).
2.1.4 Importance of Moulding Quality
As with any manufacturing system, quality is of great importance when it comes to the product and
how it is manufactured due to the high level of competition in the industry. Therefore quality has
become a market differentiator for almost any manufactured product and manufactures are constantly
looking to enhance the quality of their product. When looking at quality in the PIM process, there are
a few important aspects to consider.
Table 3: Advantages and disadvantages of PIM (Rosato, 2000).
2.1.4.1 Moulding Materials
As mentioned in Section 2.2.1 plastic will constitute the only material to be used in this project as the
CIMM is only compatible with plastic material. According to Rosato et al (2000), the general
accepted definition for plastics is: “any one of a large and varied group of macromolecular materials
Advantages Disadvantages
 High reproducibility
 Low product cost for large volume
production
 High tolerances
 Wide range of plastic materials can
be used
 Minimal scrap losses
 No (very little) finishing required
 Running costs may be high
 Parts must be designed with
moulding consideration
 Expensive equipment investment
Figure 2: Cyclic Sequence in the PIM Process (Rosato, 2000)

23. 8
consisting wholly or in part of combinations of carbon with oxygen, hydrogen, nitrogen, and other
organic and inorganic elements. Although solid in the finished state, at some stage in its manufacture
it was made liquid, and thus is capable of being formed into various shapes. This is achieved through
the application, either singly or together, of heat and pressure”.
The great economic significance of plastics is ultimately tied to their properties such as low density,
easy to process, low thermal/electronic conductivity, high chemical resistance and reusability (Zhou,
2013).
A fundamental feature of plastics is their variety. There are over 17,000 plastic materials available
worldwide and within the most common plastic families there are five major thermoplastic types
(Zhou, 2013). These thermoplastics can be categorised as; low density polyethylene (LDPE),
polyvinyl chloride (PVC), low density polyethylene (HDPE), polypropylene (PP) and polystyrene
(PS) (Zhou, 2013).
The thermoplastic that will utilised in this project is polypropylene and it is supplied in the form
plastic granules which can be seen in Figure 3 below.
2.1.4.2 Mould Requirements
In practice, the requirements of an injection mould are heavily influenced by the customer expectation
towards the quality of the product as well as the performance of the mould (Rees, 1995).
2.1.4.3 Mould Performance
Given the expensive nature of a mould investment, the development of the mould is done with the
anticipation for it to have a useful lifetime (Avery, 1998). When considering the reliability of its
Figure 3: Polypropylene granules.

24. 9
operation and life expectancy, as well as product quality and cost, mould performance is a measure of
its productivity. The productivity of a mould usually relates to the ability of the mould to produce a
certain number of products during a given timeframe (Rees, 1995).
2.1.4.4 Accuracy and Finish
Generally, the customer has two expectations when it comes to accuracy and finish: (1) parts
produced from a mould are dimensionally accurate by being within the requested tolerances, (2) the
moulded part complies with the specified finish or appearance (Rees, 1995). Therefore, it is
important to understand and consider the shrinkage of the plastic material used in order to
accommodate allowable cavity oversize for shrinkage (Rees, 1995).
From a mould design perspective, engineers will decide on the number of cavities needed for the
mould to successfully meet the customer requirements (Rees, 1995). In addition to this the engineers
will include a runner into the mould design, which serves as a channel for the molten plastic to flow
through on its way to the mould cavity. From a quality aspect, the runner ensures that molten material
can be packed into the cavity as it cools without any restriction (Rosato et al, 2000).
Figure 4 above shows the mould that will used for the manufacturing parts needed to conduct the
experiment in this project. From the figure it is evident that only one cavity will be used to mould the
part and this cavity will be supplied by a very large runner.
Runner
Mould
Cavity
Figure 4: Mould for the part that will be manufactured for this project

25. 10
2.2 Design of Experiments
When conducting an experiment there are a few points to note before ‘just jumping in’ and
undertaking the experiment at hand. Juran & Godfrey (1998) describe these points as requisites and
tools that are necessary for sound experimentation and have summarised them in Table 4 below. This
checklist can be helpful in all phases of the experiment.
Table 4 discusses choosing ‘factors’ when defining the objectives of the experiment. A factor or
parameter is one of the controlled or uncontrolled variables whose influence upon a response is being
studied in the experiment (Juran & Godfrey, 1998). Each parameter may be quantitative (e.g.
temperature in degrees) or it may be qualitative (e.g. different machines, switch on or off). ‘Level’ is
another term that needs to be addressed, the levels of a parameter are the values of the parameter
being examined in the experiment (Juran & Godfrey). For example if the experiment is to be
conducted at three different speeds, then the parameter ‘speed’ has ‘three’ levels.
A very important aspect of conducting a sound experiment is to collect accurate data, and this is
effectively achieved by the principle of replication. Juan & Godfrey (1998) define replication as the
rerunning of an experiment or measurement in order to increase precision or to provide the means for
measuring precision. A single observation or experimental run comprises a single replicate.
Replication provides an opportunity for the effects of uncontrolled factors to balance out and thus acts
Table 4: Requisites and Tools for Sound Experimentation (Juran & Godfrey, 1998).

26. 11
as a bias-decreasing tool. In order to collect accurate data in this project, each experiments level will
be rerun several times.
Juran & Godfrey (1998) define ‘design of experiments’ or DoE as an organised, statistical approach
that varies all parameters simultaneously to significantly reduce the number of experiments. With
DoE the entire experimental space can be explored efficiently by taking into account important
process parameters. The resulting data are used to generate a statistical model which is analysed to
support decision making. The areas where DoE is used in industrial research, development and
production include:
 Screening: to determine which parameters are important in the process
 Optimization: to find the optimal parameter settings for the process
 Robustness testing: to investigate how adjusting different parameters affects the process
Juran & Godfrey also classified all the various experimental design techniques and their type of
application in a table which can be found in Appendix B. The table also mentions the structure of each
design type and the information that must be sort to adequately satisfy that particular design.
For the purpose of this project, a ‘Factorial’ design (second row of Table B.1) otherwise known as
“Full Factorial” design will be implemented. The three parameters that will be investigated (Namely:
molten plastic temperature (MT), molten plastic filling time (FT) and mould packing time (PT) as
mentioned above in the problem statement.) will be tested at k levels. Therefore the number of
experimental runs that will have to be conducted for this project, due to full factorial design, can be
determined by:
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑅𝑢𝑛𝑠 = 3 𝑘
(2.1)
It will be important to note the interaction between these parameters as the objective of this industrial
research project is to find the optimal parameter settings for the injection moulding process.
2.3 Taguchi Optimization Method
Finding the optimal operational parameters of the CIMM forms the main focus of this final year
project and there are various optimization algorithms available to achieve this. DoE techniques,
especially the Taguchi Method, are widely used to generate meaningful data and determine optimal
process parameters for injection moulding (Zhou, 2013).

27. 12
The Taguchi Method is a statistical method developed by Genichi Taguchi to improve the quality of
manufactured goods. Taguchi's approach provides a systematic and efficient method for determining
near optimum operating parameters for performance and cost (Unal & Dean, 1991). Figure 5 below
illustrates a flow chart of the Taguchi approach, as explained by Unal & Dean (1991).
The details of these steps will now be communicated as explained by Unal & Dean (1991).
1. The first step in the Taguchi Method is to determine the quality characteristic to be optimized.
The quality characteristic is a parameter whose variation has a critical effect on product
quality.
2. The next step is to identify the noise factors that can have a negative impact on system
performance and quality. Noise factors are those parameters which are either uncontrollable
or are too expensive to control.
3. The third step is to identify the control parameters thought to have significant effects on the
quality characteristic. Control parameters are those design factors that can be set and
maintained. The levels for each test parameter must be chosen at this point.
4. The next step is to design the matrix experiment and define the data analysis procedure. First,
an appropriate orthogonal array (OA) for the noise and control parameters to fit a specific
study are selected. Taguchi provides many standard OA’s and corresponding linear graphs for
this purpose. Taguchi proposes OA based simulation to evaluate the mean and the variance of
Figure 5: Taguchi method flow chart (Unal & Dean, 1991).

28. 13
a product's response resulting from variations in noise factors. Figure 6 displays the OA based
simulation algorithm and structure.
5. The next step is to conduct the matrix experiment and record the results.
6. After the experiments have been conducted, the optimal test parameter configuration within
the experiment design must be determined. To analyse the results, the Taguchi Method uses a
statistical measure of performance called signal to-noise (S/N) ratio. The S/N ratio developed
by Taguchi is a performance measure to choose control levels that best cope with noise. In its
simplest form, the S/N ratio is the ratio of the mean (signal) to the standard deviation (noise).
There are three standard S/N ratios that can be used depending on the quality characteristic to
be optimized. The three different S/N ratios are:
 Biggest-is-best quality characteristic
 Smallest-is-best quality characteristic
 Nominal-is-best quality characteristic
Whatever the type of quality characteristic is chosen, the transformations are such that the
S/N ratio is always interpreted in the same way: the larger the S/N ratio the better.
Figure 6: Orthogonal Array (OA) Based Simulation Algorithm (Unal & Dean, 1991).

29. 14
7. For the final step, an experimental confirmation is run using the predicted optimum levels for
the control parameters being studied.
The Taguchi method may not necessarily provide the optimal solution as the experiment does not
contain all the possible combinations of parameters. However, it will provide a clear indication of
which parameters have the greatest effect on quality and cost. In this project, the Taguchi Method will
be implemented in conjunction with a full factorial experimental design to determine the optimal
operating regime of the CIMM.
2.4 RStudio
The software environment R is widely used for statistical computing and constructing graphics. It is
an easy to adopt coding language that allows for a user-created interface designed around a specific
(set of) problem(s) (Le Roux & Lubbe, 2013). RSudio provides the interface for the open source
statistical software package R.
2.4.1 R as a Software Development Platform
R is an open-source software system that is supported by a group of volunteers from many countries
with the central control being in the hands of a group called ‘R-Core’. Its base system provides a
general computer language for performing tasks like organising data, statistical analysis, model-
fitting, data visualisation, building of complex graphs etc. (Chambers, 2008). The R package hosts a
powerful and flexible set of statistical tools which are customisable on the platform to best suit the
required needs of the user. The platform itself facilitates both the handling and storage of data by
making use of a coherent collection of intermediate tools to analyse the data with (Chambers, 2008).
Williams (2012) has analysed R and compiled a list of advantages and disadvantages for the statistical
software system and some of those points are listed in the table on the following page.

30. 15
Table 5: Advantages and disadvantages of R (Williams, 2012).
Advantages Disadvantages
R is the most comprehensive statistical analysis
package available. It incorporates all of the
standard statistical tests, models, and analyses, as
well as providing a comprehensive language for
managing and manipulating data. New technology
and ideas often appear first in R.
R has a steep learning curve (it does take a while
to get used to the power of R) but no steeper than
for other statistical languages. R is not so easy to
use for the novice. There are several simple-to
use graphical user interfaces (GUIs) for R that
encompass point and-click interactions, but they
generally do not have the polish of the commercial
offerings.
R is a programming language and environment
developed for statistical analysis by practising
statisticians and researchers. It reflects well on a
very competent community of computational
statisticians. R is now maintained by a core team
of some 19 developers, including some very senior
statisticians.
There is, in general, no one to complain to if
something doesn’t work. R is a software
application that many people freely devote their
own time to developing. Problems are usually
dealt with quickly on the open mailing lists, and
bugs disappear with lightning speed. Users who do
require it can purchase support from a number of
vendors internationally.
The graphical capabilities of R are outstanding,
providing a fully programmable graphics language
that surpasses most other statistical and graphical
packages. Because R is open source, unlike closed
source software, it has been reviewed by many
internationally renowned statisticians and
computational scientists.
Many R commands give little thought to memory
management, and so R can very quickly consume
all available memory. This can be a restriction
when doing data mining. There are various
solutions, including using 64 bit operating systems
that can access much more memory than 32 bit
ones.
R has over 4800 packages available from multiple
repositories specializing in topics like
econometrics, data mining, spatial analysis, and
bio-informatics.
Documentation is sometimes patchy and terse, and
impenetrable to the non-statistician. However,
some very high-standard books are increasingly
plugging the documentation gaps.
2.4.2 Design of Experiments in R
As a result of being an open-source system, R is exposed to continual scrutiny by the users. This
includes some algorithms for numerical computations and simulation that likewise reflect modern,
open-source computational standards in these fields (Chambers, 2008). This means that users not only
update current algorithms that solve long standing problems, but they also develop algorithm
packages that solve the problems of today. Essentially users can create packages to solve almost any
statistical problem that they can come up with. Looking at the problem related to this project, a
‘Design of Experiments’ is one such package that exists (Cano et al, 2012).
R is the software package that will be used for the statistical analysis of this project due to its
versatility and customisability.

31. 16
2.5 Techno-economic Study
The assessment of the CIMM for its techno-economic feasibility is of utmost importance for the
motivation of the machine to be implemented into the business sector. This section will discuss the
techno-economic factors of the CIMM which consists of two stages; firstly the cost per part followed
by the break-even analysis.
2.5.1 Cost per part
One of the main objectives to this project is to successfully obtain the optimal operating settings for
the CIMM. This can be more accurately achieved by determining the cost per part, at each respective
operational setting combination, as this adds an extra dimension to finding an optimal solution. There
is no existing literature form this section of the report as the cost per part is unique to this project.
Each unique setting combination will be accounted for by producing a number of parts, for that
combination, in an experimental run. Factors that influence the cost per part are:
 Energy consumption
 Maintenance
 Raw material
 Compressed air
 Labour
The CIMM’s compressed air usage and maintenance costs are not significantly affected by varying
the operational settings, so the incurred cost can be ignored. Therefore, for the purpose of this project,
only the energy, material and labour cost will be considered in determining the cost per part for the
CIMM.
The total labour cost (𝐶𝐿,𝑇𝑜𝑡) will be calculated by taking into account both the cycle and rework time
for each part, in an experimental run, and multiplying it by the labour cost per hour. The cycle time
per part will be obtained by timing the entire run from when the first part starts to mould until the
twenty-fifth part has been moulded. For each experimental run this cost can be represented in the
following equation:
𝐶𝐿,𝑇𝑜𝑡 = (𝑇𝐶 + 𝑇𝑅) × 𝐶𝐿 (2.2)
where 𝑇𝐶 is the cycle time per part, 𝑇𝑅 is the rework time per part and 𝐶𝐿 is the unit labour cost
measured as R/hr. From this result the labour cost per part (𝐶𝐿,𝑝𝑎𝑟𝑡) can be calculated by dividing the
labour cost by the number of conforming parts produced in that run (𝑛):
𝐶𝐿,𝑝𝑎𝑟𝑡 =
𝐶 𝐿,𝑇𝑜𝑡
𝑛
(2.3)

32. 17
The manner in which the rework time per part shall be obtained is explained in Section 3.2.2.
Material costs forms the second aspect in the cost per part analysis. The total material cost (𝐶 𝑀,𝑇𝑜𝑡)
will be calculated by making use of the total part weight (𝑃𝑊) and the raw material cost (𝐶 𝑅𝑀) per
kilogram:
𝐶 𝑀,𝑇𝑜𝑡 = 𝐶 𝑅𝑀 × 𝑃𝑊 (2.4)
The material cost per part ( 𝐶 𝑀,𝑝𝑎𝑟𝑡) can then be calculated by dividing 𝐶 𝑀,𝑇𝑜𝑡 by the number of
conforming parts produced in that run (𝑛) as shown in the equation:
𝐶 𝑀,𝑝𝑎𝑟𝑡 =
𝐶 𝑀,𝑇𝑜𝑡
𝑛
(2.5)
The final cost that will be taken into consideration, for the techno-economic assessment, will be the
energy consumption cost. This consumption will be obtained by using an adaptor device (Appendix
E) which connects to the mains of the machine. This device will read and measure the total energy
consumption (𝐸 𝑇𝑜𝑡 ) of the CIMM. The total energy usage cost (𝐶 𝐸,𝑇𝑜𝑡) will then be calculated by
taking the energy consumption measurement for the run and multiplying it with the cost per kWh
( 𝐶 𝑘𝑊ℎ ) given by the local municipality. This can be depicted with the equation:
𝐶 𝐸,𝑇𝑜𝑡 = 𝐶 𝑘𝑊ℎ × 𝐸 𝑇𝑜𝑡 (2.6)
The energy cost per part (𝐶 𝐸,𝑝𝑎𝑟𝑡) can then be determined by dividing (𝐶 𝐸,𝑇𝑜𝑡) by the number of
conforming parts produced in that run (𝑛):
𝐶 𝐸,𝑝𝑎𝑟𝑡 =
𝐶 𝐸,𝑇𝑜𝑡
𝑛
(2.7)
The above costs can then be combined to determine the total cost per part (𝑇𝐶 𝑝𝑎𝑟𝑡) for each
experimental run with the equation:
𝑇𝐶 𝑝𝑎𝑟𝑡 = 𝐶𝐿,𝑝𝑎𝑟𝑡 + 𝐶 𝑀,𝑝𝑎𝑟𝑡 + 𝐶 𝐸,𝑝𝑎𝑟𝑡 (2.8)
Substituting Equations 2.2 – 2.7 into Equation 2.8 will result in the finalised 𝑇𝐶 𝑝𝑎𝑟𝑡 given below in
Equation 2.9:
𝑇𝐶 𝑝𝑎𝑟𝑡 =
(𝐶 𝐿)(𝑇 𝐶+ 𝑇 𝑅)+(𝐶 𝑘𝑊ℎ)(𝐸 𝑇𝑜𝑡)+(𝐶 𝑅𝑀)(𝑇𝑜𝑡 𝑃𝑊)
𝑛
(2.9)

33. 18
2.5.2 Break-even Analysis
Gutierrez and Dalsted (2012) provide a sufficient definition for break-even analysis: “Break- even
analysis is a useful tool to study the relationship between fixed costs, variable costs and returns. A
break-even point (BEP) defines when an investment will generate a positive return”. As its name
implies, this approach determines the sales needed to break even.
From a calculation perspective, the break-even point computes the volume of production at a given
price necessary to cover all costs (Gutierrez and Dalsted, 2012). The formula for this calculation is
given as:
𝐵𝐸𝑃 =
𝐹𝑖𝑥𝑒𝑑 𝐶𝑜𝑠𝑡𝑠
𝑆𝑃−𝑉𝐶
(2.8)
where SP is the selling price per part and VC is the variable cost per part.
In the case of this project, the fixed costs will only involve the cost of the CIMM and the VC will
include the three costs (𝑇𝐶 𝑝𝑎𝑟𝑡) mentioned above in Section 2.6.1. From the result obtained in the
break-even analysis, a feasibility case can be provided for purchasing the CIMM.

34. 19
3. Project Methodology
The project methodology will comprise of two sections; the experimental procedure followed by the
data collection and analysis methodology. These two sections will be outlined and discussed in this
chapter.
3.1 Experimental Procedure
In order to determine an optimal operating setting for the CIMM, experiments need to be executed.
These experiments will be performed by using the Taguchi Method in conjunction will a full factorial
experimental design.
The steps of the Taguchi method will now be implemented.
3.1.1 Quality Aspect to be Optimised
The quality aspect that is to be optimised is the moulding finish of plastically moulded parts using the
CIMM. The side effects of this optimising process will include moulded parts with varying levels of
quality to the finished part. Each of these parts will be classified as either a conforming or non-
forming part.
3.1.2 Identify the Noise Factors and Test Conditions
The experiments will be conducted in the Senrob Lab of the Mechanical and Industrial Engineering
Building. As explained in the Taguchi Optimization Method (Section 2.3), it is important to identify
the noise factors in this experiment as they can have a negative impact on the quality of the moulded
parts. The noise factors that could affect the mould operation on the CIMM are:
 Variation in the raw material (plastic granules)
 Machine condition
 Ambient temperature of the Senrob Lab
 Operator skill
3.1.3 Identify the Control Factors and their Alternative Levels
As mentioned in step 3 of the Taguchi Method, the control factors (test parameters) are those that can
be set and maintained. Recapitulating from Section 1.2, the control factors are as follows:
 The temperature of the molten plastic, or more simply the mould temperature (MT).
 The time allocated for the molten plastic to flow into the mould, or more simply the filling
time (FT).
 The time allocated for the mould to be kept in place before the part is ejected, or more simply
the packing time (PT).

35. 20
The factors and their levels, for conducting the experiment, were decided upon by moulding a few
parts at random levels until several conforming parts were successfully produced. The control
parameters settings were noted and the levels for the experiment were consequently chosen based on
moderate variations on the noted parameter settings. The control factors and their respective levels for
experimentation are shown in Table 6.
3.1.4 Design Experimental Matrix
An appropriate sized orthogonal array (OA) has to be used for conducting the experiments. Given that
a full factorial experimental design is to be executed, Equation 2.1 will be used to determine the size
of the (OA). This equation yields:
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑅𝑢𝑛𝑠 = 33
= 27
Therefore the most suitable orthogonal array for experimentation is an L27 array as shown in Table 7
on the next page. This means that a total of twenty seven experiments need to be carried out.
1 2 3
MT 180 190 200
FT 5 10 15
PT 3 5 7
Factors
Levels
Table 6: Control factors and their levels for experimentation.

36. 21
Table 7: The L27 Orthogonal Array (OA)
3.1.5 Conduct Matrix Experiment
In accordance with the above OA, experiments were conducted with the factors and their levels as
mentioned in Table 7. The experimental layout with the selected values of the factors is shown on the
following page in Table 8.
Experimental
No.
Control Factors
1 2 3
1 1 1 1
2 1 1 2
3 1 1 3
4 1 2 1
5 1 2 2
6 1 2 3
7 1 3 1
8 1 3 2
9 1 3 3
10 2 1 1
11 2 1 2
12 2 1 3
13 2 2 1
14 2 2 2
15 2 2 3
16 2 3 1
17 2 3 2
18 2 3 3
19 3 1 1
20 3 1 2
21 3 1 3
22 3 2 1
23 3 2 2
24 3 2 3
25 3 3 1
26 3 3 2
27 3 3 3

37. 22
Table 8: OA with Control Factors and their different levels for Experimentation.
Experimental
No.
Control Factors
MT FT PT
1 180 5 3
2 180 5 5
3 180 5 7
4 180 10 3
5 180 10 5
6 180 10 7
7 180 15 3
8 180 15 5
9 180 15 7
10 190 5 3
11 190 5 5
12 190 5 7
13 190 10 3
14 190 10 5
15 190 10 7
16 190 15 3
17 190 15 5
18 190 15 7
19 200 5 3
20 200 5 5
21 200 5 7
22 200 10 3
23 200 10 5
24 200 10 7
25 200 15 3
26 200 15 5
27 200 15 7
Each of the above 27 experiments will involve manufacturing 25 parts as to account for the variations
that may occur due to the noise factors. This means that a total of 675 moulds will be manufactured
by the CIMM during the experimentation process. The experiments shall be executed in the order they
are represented in Table 8. One of these experiments will represent the optimal operational settings
for the CIMM to produce the given part.

38. 23
The manner in which each of these 27 experiments will be executed is visually outlined below in
Figure 7.
Figure 7: Flow chart of the experimental procedure.

39. 24
As mentioned in 7, each moulded part will be placed on a sequential numbering chart as it is removed
from the CIMM as they are too hot mark with a permanent marker immediately after their removal.
The reason for numbering system is to track the quality of parts as the experiment is conducted to see
if any trends are identified. An image of the sequential chart is shown in Figure 8 below.
Once an experimental run has been completed and the total cycle time and power usage for that run
have been recorded, each part will be numbered with permanent marker. This number must correlate
with position on the chart.
Following the numbering procedure, the parts for each experimental run will then be placed into a bag
labelled with that experiments number. The data that has to be collected from these experiments and
the manner in which it is to be obtained will be discussed in the succeeding section.
Figure 8: The sequential numbering chart.

40. 25
3.2 Data Collection and Analysis Methodology
This section will look at how the data was obtained from the moulded parts that were produced during
the experiments and the quality inspection procedure that was followed.
3.2.1 Quality Inspection Criteria
The operational settings can only be considered optimal if conforming parts are produced and this can
be determined through a quality inspection. The quality inspection of each part was divided into four
inspection criteria:
1. Visual inspection of part conformance
2. Rework time, measured in seconds (sec)
 Rework time for runner (𝑇𝑅,𝑟𝑢𝑛𝑛𝑒𝑟)
 Rework time for finishing (𝑇𝑅,𝑓𝑖𝑛𝑖𝑠ℎ)
3. Part weight, measured in grams (g)
4. Part thickness, measured in millimetres (mm)
Each of the above criteria will now be discussed and insight will be given as to how each was
measured.
3.2.2 Quantitative Analysis of Part Quality
As previously mentioned, visual inspection of part conformance formed the first criteria of the quality
inspection process. This aspect of the inspection process is important as it determines whether a part
was successfully been moulded or not. The result will have significant implications to the cost per part
value for that experimental run as shown in the equations from Section 2.5.1. An example of a
conforming part prior to rework, and non-conforming part can be seen in Figure 9 and Figure 10
respectively on the following page.

41. 26
It is important to note that conformance is based on the circular section of the moulded part as it is the
circular section which constitutes the final part. The longitudinal section is the runner of the moulded
part which will be cut off during the rework cycle.
The second criteria, rework time, was measured for the purpose of determining the labour costs for
the production of the part. For the purposes of this project the rework time will involve two
dimensions; rework time for the runner (𝑇𝑅,𝑟𝑢𝑛𝑛𝑒𝑟), and rework time for the finishing (𝑇𝑅,𝑓𝑖𝑛𝑖𝑠ℎ) of the
part. Rework is a necessary process as it ensures the final part meets the design specifications. This is
a standard time and was determined by measuring the time to rework twenty-five parts which was
repeated three time to get an average. An average rework time per part was obtained from dividing the
average by twenty-five, as there were twenty-five parts produced per experiment.
Figure 9: A visually conforming part, prior to rework.
Figure 10: A visually non-conforming part.

42. 27
The standard rework time of how long it will take to rework a single part was based on the average
rework time per part. The results of this are shown in Table 9 below.
Only conforming parts, as shown in Figure 9, will undergo rework as there is no point wasting time
on reworking non-conforming points as it will not add any value to the part. Figure 11 and 12 below
show a conforming part following rework for the runner and finishing respectively.
The third criteria is the part weight (PW) and it was measured using a very accurate scale which
measures to one-thousandth of a gram (10-3
g) or three decimal places. It is important to mention that
conforming parts were weighed before (PW) and after both rework procedures (PWA) had been
executed in order to get a measurement for the wasted material per part. This is illustrated in Figure
13 and 14 on the next page.
Trial 1 Trial 2 Trial 3
Runner 137 128 134 133 5.32 6
Finishing 368 356 377 367 14.68 15
Rework Dimension
Times (sec) Average Time
per Trial
Average Rework
Time per Part
Standard Rework
Time per Part
Table 9: Table showing how the standard rework times were determined.
Figure 12: Conforming part following rework for
finishing.
Figure 11: Conforming part following rework for
runner.

43. 28
The fourth and final criteria is the part thickness which was measured using a very accurate digital
vernier calliper which can also measure to one-thousandth of a millimetre (10-3
mm) or three decimal
places. The thickness measurement was chosen at an arbitrary point as the part had no existing
dimensional tolerances.
Non-conforming parts were not given a thickness measurement as they have already been declared as
not being useful. Figure 15 on the following page shows where and how the part thickness was
measured.
Figure 13: Weighing part prior to rework (PW). Figure 14: Weighing part after rework (PWA).

44. 29
All the data that was measured from implementing the four inspection criteria was captured and
entered into excel sheets for each experiment. An example of the data that was captured can be seen
below in Table 10 which displays an excel sheet with the data that was captured for Experiment 8.
This table is displayed on the page that follows.
Figure 15: Illustrating where and how the part thickness was measured using the digital vernier calliper.

45. 30
Table 10: The data collected for experiment 8 is shown here as an example of the data collected for each experiment.
Visually conforming and non-conforming parts are represented with a ‘1’ and ‘0’ respectively in the
‘Conformance’ column. The ‘Waste Material’ column values were achieved by subtracting the PWA
from the PW.
Once the data for all the experiments have been captured into excel sheets, the statistical analysis of
this data can commence. Chapter 4 outlines the steps followed and results obtained from the techno-
economic analysis and statistical analysis.
Experiment No. 8
Temp (o
C) 180 Cycle Time (min) 13:54
FT (sec) 15 Power Usage (kWh) 0.0656
PT (sec) 5
Time Started 13:09
1 0 0.607 0.607
2 0 1.589 1.589
3 0 1.751 1.751
4 1 6 15 1.810 0.873 0.937 2.254
5 1 6 15 1.900 0.896 1.004 2.287
6 1 6 15 1.876 0.907 0.969 2.305
7 1 6 15 1.917 0.922 0.995 2.332
8 1 6 15 1.880 0.914 0.966 2.337
9 1 6 15 1.914 0.915 0.999 2.313
10 1 6 15 1.817 0.914 0.903 2.354
11 1 6 15 1.854 0.912 0.942 2.355
12 1 6 15 1.852 0.914 0.938 2.324
13 1 6 15 1.950 0.933 1.017 2.379
14 1 6 15 1.861 0.920 0.941 2.348
15 1 6 15 1.877 0.917 0.960 2.322
16 1 6 15 1.906 0.906 1.000 2.298
17 1 6 15 1.879 0.912 0.967 2.319
18 1 6 15 1.852 0.916 0.936 2.309
19 1 6 15 1.861 0.927 0.934 2.325
20 1 6 15 1.873 0.928 0.945 2.328
21 1 6 15 1.899 0.918 0.981 2.321
22 1 6 15 1.842 0.922 0.920 2.317
23 1 6 15 1.855 0.920 0.935 2.350
24 1 6 15 1.866 0.915 0.951 2.320
25 1 6 15 1.930 0.918 1.012 2.326
Thicknes
s (mm)
Part No.
Conformance
(0/1)
Rework Time
(Runner)
Rework Time
(Finish)
PW (g) PWA (g)
Waste
Material (g)

46. 31
4. Analysis of Data
To successfully determine the optimal operational settings for the CIMM the data collected in Section
3.2 has to statistically analysed. The following section will review the procedures that were used to
analyse the data that was captured into Excel. Graphs that could not be created in RStudio will be
created in Excel. A copy of the R coding that was used in this project can be found in Appendix D.
Three different analyses will be used:
 Initial observation analysis
 Techno-economic analysis
 Statistical analysis
 Descriptive statistical analysis
 ANOVA analysis
 Process control analysis
 Waste Analysis
The two main objectives of the analysis in this section are:
1. Total cost per part (𝑇𝐶 𝑝𝑎𝑟𝑡) must be minimised
2. Part thickness must be maximised
These two objectives have to be achieved whilst minimising the variation of part thickness in
moulding process.
4.1 Initial Observation Analysis
Once all the data had been captured into the excel sheets for all the conducted experiments, two initial
observations were noted before any statistical analysis needed to be performed. The first observation
pertained to the number of conforming parts that were produced for each of the conducted
experiments. A summary of these results can be seen in Figure 16 on the following page.
The second observation was that the moulded parts did not automatically eject as expected. This not
only meant that the experimental runs acquired a greater cycle time (𝑇𝐶), it also posed a safety hazard
as the parts now had to be removed by hand as shown in Figure 17 on the following page.

47. 32
From Figure 16 it is evident that only “Experiments; 7, 8, 9, 13, 14, 15, 16, 17, 18, 22, 23, 24, 25, 26,
27” produced conforming parts. Therefore, only these experiments will be considered for the techno-
economic and statistical analyses that follow.
Figure 16: A bar graph showing the number of conforming parts produced per experiment.
Figure 17: Removing moulded parts by hand.

48. 33
4.2 Techno-economic Analysis
The first part of the techno-economic analysis involves determining the cost per part for each
experiment as outlined in Section 2.5. These values are needed for the second stage of the techno-
economic analysis, namely the break-even analysis. Section 2.5.1 stated that only the energy
consumption, labour and raw material costs would be considered for the techno-economic analysis
and their unit costs are summarised in the following table:
Substituting the above unit costs along with the captured data into Equations 2.2, 2.4 and 2.6 will
result in the total costs for labour (𝐶𝐿,𝑇𝑜𝑡), material (𝐶 𝑀,𝑇𝑜𝑡) and energy (𝐶 𝐸,𝑇𝑜𝑡) respectively. These
values can been seen in Table 12 below:
Cost Parameters Unit Source
Energy (CkWh) R0.88/kWh Stellenbosch Municipality, 2015
Labour (CL) R85/hr Trading Economics Website, 2015
Material (CRM) R20.75/Kg Plastomark PTY LTD Quote, 2015
Table 11: Unit costs for the measured cost parameters.
Usage (kWh) Cost (R ) Usage (hr) Cost (R ) Usage (Kg) Cost (R )
7 0.0677 0.06 0.35 29.54 0.045351 0.94 30.54 19 1.61
8 0.0656 0.06 0.36 30.60 0.045218 0.94 31.60 22 1.44
9 0.0651 0.06 0.37 31.04 0.045824 0.95 32.05 21 1.53
13 0.0537 0.05 0.32 27.08 0.045402 0.94 28.07 22 1.28
14 0.0598 0.05 0.34 29.27 0.048003 1.00 30.32 24 1.26
15 0.0592 0.05 0.34 28.74 0.046316 0.96 29.75 22 1.35
16 0.0725 0.06 0.39 33.43 0.046267 0.96 34.45 24 1.44
17 0.0733 0.06 0.39 33.52 0.047838 0.99 34.58 24 1.44
18 0.0746 0.07 0.40 34.33 0.047669 0.99 35.39 25 1.42
22 0.0618 0.05 0.35 29.54 0.047371 0.98 30.57 24 1.27
23 0.0639 0.06 0.36 30.34 0.048262 1.00 31.39 25 1.26
24 0.0648 0.06 0.36 30.60 0.046800 0.97 31.63 25 1.27
25 0.0875 0.08 0.42 35.96 0.046611 0.97 37.01 25 1.48
26 0.0991 0.09 0.48 40.68 0.045047 0.93 41.70 25 1.67
27 0.0996 0.09 0.48 40.80 0.045971 0.95 41.84 25 1.67
Total Cost
per part (R )
Costs
Experiment Energy (CE,Tot) Labour (CL,Tot) Material (CM,Tot) Total
Cost (R )
n
Table 12: Summary of techno-economic analysis.

49. 34
The total cost per part (𝑇𝐶 𝑝𝑎𝑟𝑡) for each experimental run can then be calculated by using Equation
2.9 and converting input values in the following way:
𝑇𝐶 𝑝𝑎𝑟𝑡 =
(𝐶 𝐿)(
𝑇 𝐶
60
+
𝑇 𝑅
3600
)+(𝐶 𝑘𝑊ℎ)(𝐸 𝑇𝑜𝑡)+(𝐶 𝑅𝑀)(
𝑇𝑜𝑡 𝑃𝑊
1000
)
𝑛
These conversions had to be made in order to get the cycle time and rework times into hours along
with the total part weight into kilograms as these are the units in which the cost parameters are
supplied. The 𝑇𝐶 𝑝𝑎𝑟𝑡 values constitute the main focus of the techno-economic analysis and they can
be found in the final column of Table 12. A summary of these results can be illustrated in the
histogram that follows below.
From Table 12 and Figure 18 it can be observed that five of the fifteen experiments have a total cost
per part value between R1.20 and R1.30, the lowest interval. It can also be noted that the lowest total
cost per part is R1.26 achieved by Experiment 14 and Experiment 23.
4.3 Statistical Analysis
A statistical analysis of the collected data will be discussed in the following section. The statistical
analysis was executed with the assistance of RStudio (an open source software package for the R
environment).
4.3.1 Descriptive Statistical Analysis
A basic summary of the measurable parameters and collected data is given in Figure 19 as this is a
good way to ensure data integrity. The data only relates to the conforming parts that were produced in
the experimental runs. This is due to the purpose of the project, namely finding the optimal
operational settings of the CIMM which involves maximising the number of conforming parts
produced.
Figure 18: Histogram showing the distribution of total cost per
part.

50. 35
Looking at the Figure 19 above and recapitulating from Section 3.1.3; the MT, FT and PT are the
moulding temperature (o
C), filling time (sec) and packing time (sec) respectively. Together they form
the control factors (or parameters) of the CIMM. PW, PWA, Waste and Thickness are the measurable
data that was captured during the experimental runs in Section 3.2. In Figure 19; PW, PWA and
Waste are represented in grams while Thickness is represented in millimetres. The final column TC is
the total cost per part (𝑇𝐶 𝑝𝑎𝑟𝑡) that was calculated in Section 4.2 and it is represented in Rands.
In order to ensure a thorough descriptive analysis of the data is conducted, various plots were
generated in RStudio that are illustrated throughout this chapter. These plots compare the
relationships of the control parameters with the two main objectives that need to be met, namely
minimising 𝑇𝐶 𝑝𝑎𝑟𝑡 and maximising part thickness. Box plots were the first chosen graph-type as they
show variation which is another criteria that has to be minimised as this indicates how controlled the
process is.
The first set of graphs look to compare the control parameters against the first objective, namely
minimising 𝑇𝐶 𝑝𝑎𝑟𝑡.
Figure 19: Statistical summary of measurable parameters and collected data.
Figure 20: Total cost per part as a function of mould temperature.

51. 36
Box plots of the 𝑇𝐶 𝑝𝑎𝑟𝑡 versus mould temperature, filling and packing time are displayed in Figure
20, 21 and 22 respectively. Looking at these box plots from a variation perspective it is clear that:
 Increasing the mould temperature from 180o
C to 190o
C results in very little change in
variation but a further increase to 200o
C produces a greater variation
 Increasing the filling time from 10 seconds to 15 seconds results in a greater variation
 Increasing the packing time from 3 seconds to 5 seconds produces a slight increase in
variation but there is no significant variation change between 5 and 10 seconds
Figure 22: Total cost per part as a function of packing time.
Figure 21: Total cost per part as a function of filling time.

52. 37
Figure 23: Thickness as a function of mould temperature.
Based on variation of the 𝑇𝐶 𝑝𝑎𝑟𝑡 alone, the optimal operating parameters are shown in the table
below. It is important to note that these operational settings represent Experiment 13.
The second set of box plots look to compare the control parameters against the second
objective, namely maximising part thickness.
Control Parameter Setting
MT 190
FT 10
PT 3
Table 13: Optimal based on the variation of total cost per part.

53. 38
Box plots of part thickness versus mould temperature, filling and packing time are displayed in Figure
23, 24 and 25 respectively. Looking at these box plots from a variation perspective it is clear that:
 Increasing the mould temperature shows no significant relationship with regards to a constant
increase/decrease in variation of part thickness
 Increasing the filling time from 10 seconds to 15 seconds results in a slightly greater variation
of part thickness
 Increasing the packing time shows no significant relationship with regards to a constant
increase/decrease in variation of part thickness
Figure 25: Thickness as a function of packing time.
Figure 24: Thickness as a function of filling time.

54. 39
Figure 26: Total cost as a function of mould temperature with linear trend line.
Based on variation of the part thickness alone, the optimal operating parameters are shown in the table
below. It is important to note that these operational settings represent Experiment 23.
4.3.2 ANOVA Analysis
The six plots generated in Section 4.3.1 were plotted again; however, in this section they were plotted
as scatterplots and given a trend line. This trend line is necessary to successfully conduct ANOVA
(Analysis of Variance) which will test to see if there is a linear relationship between the three control
parameters and the two main objectives mentioned in the previous section.
Once again, the first set of graphs look to compare the control parameters against the first objective,
namely minimising 𝑇𝐶 𝑝𝑎𝑟𝑡.
Control Parameter Setting
MT 200
FT 10
PT 5
Table 14: Optimal settings based on the variation of part thickness.

55. 40
Figure 27: Total cost as a function of filling time with linear trend line.
Scatterplots of the 𝑇𝐶 𝑝𝑎𝑟𝑡 versus mould temperature, filling and packing time are displayed in Figure
26, 27 and 28 respectively. Looking at the trend lines on these plots it is clear that:
 Increasing the mould temperature results in a moderate decrease in the 𝑇𝐶 𝑝𝑎𝑟𝑡
 Increasing the filling time results in a relatively large increase in the 𝑇𝐶 𝑝𝑎𝑟𝑡
 Increasing the packing time results in a slight increase in the 𝑇𝐶 𝑝𝑎𝑟𝑡
Figure 28: Total cost as a function of packing time with linear trend line.

56. 41
ANOVA was then conducted for each graph (Figure 26, 27 and 28) by using the ‘anova()’ function in
R. The aim of the ANOVA test is to statistically confirm if a linear relationship does indeed exist
between the control parameters and the 𝑇𝐶 𝑝𝑎𝑟𝑡 as mentioned in the bullet points above. A relationship
can be confirmed if the ‘Pr(> 𝐹)’ value is less than 0.05 (5%). The Pr(> 𝐹) represents the
probability of finding data along the existing trend line for the x-value on the graph, in this case the
control parameters. In other words it is the strength of the correlation between the two parameters, the
smaller the Pr(> 𝐹) value the greater the correlation. An example of the ANOVA test for the linear
test between 𝑇𝐶 𝑝𝑎𝑟𝑡 and mould temperature (MT) is shown in Figure 29 below:
Looking at the figure it can be seen that the Pr(> 𝐹) value is 0.03647 which is less than 0.05 and this
statistically confirms that a linear relationship exists. The results of the ANOVA tests for 𝑇𝐶 𝑝𝑎𝑟𝑡
against all three control parameters is shown in the table that follows. It is clear that the two remaining
Pr(> 𝐹) values are also less than 0.05 which statistically confirms all three control parameters have a
linear relationship with 𝑇𝐶 𝑝𝑎𝑟𝑡 as mentioned in the three bullet points on the previous page.
The second set of graphs look to compare the control parameters against the second objective, namely
maximising part thickness. These graphs start on the following page.
Figure 29: Summary of ANOVA test between total cost per part and mould temperature.
MT 0.03647
FT 2.2 x10
-16
PT 0.0444
Control Parameter
tested against TCpart
Pr (>F)
Table 15: ANOVA results for total cost per part.

57. 42
Figure 30: Part thickness as a function of mould temperature with linear trend line.
Figure 31: Part thickness as a function of filling time with linear trend line.

58. 43
Scatterplots of part thickness versus mould temperature, filling and packing time are displayed in
Figure 30, 31 and 32 respectively. Looking at the trend lines on these plots it is clear that:
 Increasing the mould temperature results in a slight decrease in part thickness
 Increasing the filling time results in a slight increase in part thickness
 Increasing the packing time results in a slight decrease in part thickness
Once again, ANOVA was conducted for each graph (Figure 30, 31 and 32) to statistically confirm if a
linear relationship does indeed exist between the control parameters and the part thickness as
mentioned in the bullet points above. The results of the ANOVA tests for part thickness against all
three control parameters is shown in the table that follows. It is clear that all three Pr(> 𝐹) values are
also less than 0.05 which statistically confirms all the control parameters have a linear relationship
with part thickness as mentioned in the three bullet points above.
Figure 32: Part thickness as a function of packing time with linear trend line.
MT 8.22 ×10-4
FT 9.04 ×10-8
PT 4.29 ×10-3
Control Parameter tested
against part thickness
Pr (˃F)
Table 16: ANOVA results for part thickness.

59. 44
Two separate three-dimensional scatterplots were generated and are displayed in Figure 33 and 34
below as they give a good visual representation of the data. The factors plotted were selected
according to the level of correlation (lowest Pr(> 𝐹) values) with 𝑇𝐶 𝑝𝑎𝑟𝑡 and part thickness
respectively. From Table 15 and 16 it is evident that the PT Pr(> 𝐹) values were the largest for both
𝑇𝐶 𝑝𝑎𝑟𝑡 and part thickness, hence PT had very little influence on the two objectives and was
subsequently neglected from the following two graphs.
Figure 33: 3D scatter plot showing total cost per part as a function of filling time and mould temperature.
Figure 34: 3D scatter plot showing part thickness as a function of filling time and mould temperature

60. 45
Looking at Figure 33 it is clear that an FT value of 10 seconds provides a lower 𝑇𝐶 𝑝𝑎𝑟𝑡 which will be
decreased slightly further with a MT of 180o
C. However, it must be noted that a combination of these
two control parameters did not produce any conforming parts. Therefore, in terms of 𝑇𝐶 𝑝𝑎𝑟𝑡, the
optimal operational settings are shown in Table 17. It is important to note that these operational
settings represent Experiment 13, 14 and 15.
Figure 34 shows that part thickness is not significantly affected by changing the control parameters. It
can be deduced that an FT value of 15 seconds and an MT value of 180o
C produce a slightly greater
part thickness but this comes at the expense of a very large variation. Due to the large variation shown
at the 180o
C MT value, 190o
C was chosen as the preferred MT value. The 190o
C setting along with
the additional two operational settings in the table below, show the optimal operational settings in
terms of part thickness. It is important to note that these operational settings represent Experiment 16,
17 and 18.
From Table 17 and 18 above, it is clear that the only different operational parameter is FT. A re-
examination of the three-dimensional plots shows that part thickness is not as significantly affected by
changing the FT from 10 seconds to 15 seconds as comparison to the 𝑇𝐶 𝑝𝑎𝑟𝑡. Therefore, the
operational settings in terms of 𝑇𝐶 𝑝𝑎𝑟𝑡 were selected as the optimal operational settings for the
ANOVA analysis.
4.3.3 Process Control Analysis
This section will outline the process control study that was conducted from the collected data. Process
control is the ability of a process to produce outputs that conform to the required specifications. The
process control study is visually aided with control charts of which the most common are the ‘R-
chart’ and ‘𝑥̅-chart’. The 𝑥̅-chart is used to monitor the centring of a process while the R-chart
monitors the variation of a process. A variation analysis has already been covered in Section 4.3.1 and
Section 4.3.2, therefore an 𝑥̅-chart will be used for this analysis.
Control Parameter Setting
MT 190
FT 10
PT 3,5,7
Table 17: Optimal settings for total cost per part.
Control Parameter Setting
MT 190
FT 15
PT 3,5,7
Table 18: Optimal settings for part thickness.

61. 46
An 𝑥̅-chart will be generated in Excel to analyse the CIMM’s ability to centre the part thickness, of
conforming parts, in the injection moulding process. The first step is to generate the mean thickness
value (denoted by 𝑥̅) and standard deviation value (denoted by 𝑠) for each experiment i, that
produced conforming parts. Recapitulating from section 4.1 these experiments were; “Experiments; 7,
8, 9, 13, 14, 15, 16, 17, 18, 22, 23, 24, 25, 26, 27”. Next, the overall mean (𝑥̿) and average standard
deviation (𝑠̅) calculations are made as shown in the equations below.
𝑥̿ =
∑ 𝑥̅ 𝑖
𝑘
𝑖=1
𝑘
(4.1)
𝑆̅ =
∑ 𝑠𝑖
𝑘
𝑖=1
𝑘
(4.2)
The variable 𝑘 represents the number of experimental samples, which in this case is fifteen as only
fifteen experiments produced conforming parts. The overall mean and average range are then used to
compute the center line (CL) along with the lower and upper control limits (LCL and UCL) for the 𝑥̅-
chart using the following equations:
𝐶𝐿 = 𝑥̿ (4.3)
𝐿𝐶𝐿 = 𝑥̿ − 𝐴3 𝑠̅ (4.4)
𝑈𝐶𝐿 = 𝑥̿ + 𝐴3 𝑠̅ (4.5)
The variable A3 is a constant dependant on the sample size (in this case number of conforming parts,
n, in each experiment) found in Appendix C, provided by Evans & Lindsay (2014). Due to the
different number of conforming parts per experiment (Figure 16), an average value was used. This
average value, along with the additional values calculated in Equations 4.1 – 4.5 are shown in the
table that follows.
Table 19: Summary of values used for the X-bar chart.
2.336 0.047809 2.336 2.306 2.365 23.47 24

62. 47
The resulting 𝑥̅-chart, generated in Excel, is shown in Figure 35 below.
The control limits (LCL and UCL) represent the range between which all points need to occur for the
process to be in statistical control. If an 𝑥̅ value falls outside this range it shows the CIMM was not
able to successfully centre the part thickness for the control parameters of that experiment. A
summary of this analysis is given in the table on the succeeding page.
Figure 35: X-bar chart created in Excel.

63. 48
Therefore, based on the process control analysis, the optimal operational settings are shown in the
table below.
4.4 Waste Analysis
Due to the limited amount of data points for the 𝑇𝐶 𝑝𝑎𝑟𝑡 in the statistical analysis as well as the large
wastage that was observed during that data capturing process, a wastage analysis was also conducted.
The limited amount of data points in the descriptive analysis is as a result of the energy consumption
cost and labour cost being divided by the number of conforming parts for that experiment (Equation
2.3 and 2.7). This was done as due to the difficulty of individually measuring the energy consumption
and labour time for each part.
7 Yes
8 Yes
9 No
13 No
14 Yes
15 No
16 No
17 No
18 No
22 Yes
23 Yes
24 Yes
25 No
26 Yes
27 Yes
Statistically
in Control
Experiment
Table 20: Summary of results for process control analysis.
MT FT PT
7 180 15 3
8 180 15 5
14 190 10 5
22 200 10 3
23 200 10 5
24 200 10 7
26 200 15 5
27 200 15 7
Experiment
Control Parameters
Table 21: Experiments that are statistically in control.

64. 49
However, it was possible to individually measure the material cost per part as each part was weighed
before (PW) and after rework (PWA) as mentioned in Section 3.2.2. Therefore, a double bar graph
was plotted showing the waste material compared to the actual material that was used for the finished
part (PWA). Once again only conforming parts were considered for the analysis and the generated
graph is displayed below in Figure 36.
From the graph it is evident clear that more material went to waste than that actual mould itself for
every experiment conducted, except for Experiment 26 which had the same value for both weights. A
summary of the results, given in the table on the following, shows the waste material as a percentage
of the material used in the mould.
Figure 36: Summary of waste analysis.

65. 50
Therefore, in terms of wastage, the control parameters for Experiment 26 serve as the optimal
operational settings as this experiment had the lowest wastage per conforming part.
Experiment PWA (g) Waste (g) Wastage as Percentage of PWA
7 17.434 18.024 103%
8 20.119 21.152 105%
9 18.936 20.262 107%
13 19.171 21.364 111%
14 21.207 25.103 118%
15 19.225 22.234 116%
16 22.521 23.109 103%
17 22.619 23.497 104%
18 23.463 24.206 103%
22 21.742 24.062 111%
23 22.812 25.45 112%
24 22.553 24.247 108%
25 22.856 23.755 104%
26 22.558 22.489 100%
27 22.438 23.533 105%
Table 22: Table showing wastage as a percentage of the material used for the completed part (i.e. after rework).