The Taguchi method involves reducing the variation in a process through robust design of experiments. The experimental design proposed by Taguchi involves using orthogonal arrays to organize the parameters affecting the process and the levels at which they should be varies. Instead of having to test all possible combinations like the factorial design, the Taguchi method tests pairs of combinations. The Taguchi arrays can be derived or looked up. Small arrays can be drawn out manually; large arrays can be derived from deterministic algorithms. Generally, arrays can be found online. The arrays are selected by the number of parameters (variables) and the number of levels (states).
In this paper, the specific steps involved in the application of the Taguchi method will be described with example.
Introduction to Design of Experiments by Teck Nam Ang (University of Malaya)Teck Nam Ang
This set of slides explains in a simple manner the purpose of experiment, various strategies of experiment, how to plan and design experiment, and the handling of experimental data.
PPT ON TAGUCHI METHODS / TECHNIQUES - KAUSTUBH BABREKARKaustubh Babrekar
A brief brief to Taguchi Methods / Techniques; Loss function; Orthogonal arrays; Fractional Factprials and various case studies and examples related to each topic covered in detail.
PPT presented by Kaustubh Babrekar under the guidance of Prof. Dr. N. G. Phafat. MGM JNEC Aurangabad.
Introduction to Design of Experiments by Teck Nam Ang (University of Malaya)Teck Nam Ang
This set of slides explains in a simple manner the purpose of experiment, various strategies of experiment, how to plan and design experiment, and the handling of experimental data.
PPT ON TAGUCHI METHODS / TECHNIQUES - KAUSTUBH BABREKARKaustubh Babrekar
A brief brief to Taguchi Methods / Techniques; Loss function; Orthogonal arrays; Fractional Factprials and various case studies and examples related to each topic covered in detail.
PPT presented by Kaustubh Babrekar under the guidance of Prof. Dr. N. G. Phafat. MGM JNEC Aurangabad.
Approaches to Experimentation
What is Design of Experiments
Definition of DOE
Why DOE
History of DOE
Basic DOE Example
Factors, Levels, Responses
General Model of Process or System
Interaction, Randomization, Blocking, Replication
Experiment Design Process
Types of DOE
One factorial
Two factorial
Fractional factorial
Screening experiments
Calculation of Alias
DOE Selection Guide
The presentation depicted herein presents briefly an introduction of acceptance sampling along with some major differences amongst the widely used sampling standards.
Acceptance Sampling standards comparison. MIL-STD-105E, MIL-STD-1916, ISO 2859, ISO 3951. About AQLs and OC Curves.
Experimental methods are widely used in industrial settings and research activities. In industrial settings, the main goal is to extract the maximum amount of unbiased information regarding the factors affecting production process form few observations, whereas in research, ANOVA techniques are used to reveal the reality. Drawing inferences from the experimental result is an important step in design process of product. Therefore, proper planning of experimentation is the precondition for accurate conclusion drawn from the experimental findings. Design of experiment is powerful statistical tool introduced by R.A. Fisher in England in the early 1920 to study the effect of different parameters affecting the mean and variance of a process performance characteristics
Taguchi's orthogonal arrays are highly fractional orthogonal designs. These designs can be used to estimate main effects using only a few experimental runs.
Consider the L4 array shown in the next Figure. The L4 array is denoted as L4(2^3).
L4 means the array requires 4 runs. 2^3 indicates that the design estimates up to three main effects at 2 levels each. The L4 array can be used to estimate three main effects using four runs provided that the twthree-factoro factor and three factor interactions can be ignored.
According to WHO (World Health Organization):
“QA is the activity of providing evidence needed to establish confidence among all concerned that quality related activities are being performed effectively.”
According to ISO:
“All those planned and systematic activities implemented to provide adequate confidence that an entity will fulfill requirements for quality.
Design of Experiment (DOE): Taguchi Method and Full Factorial Design in Surfa...Ahmad Syafiq
Taguchi and full factorial design techniques to highlight the application and to compare the effectiveness of the Taguchi and full factorial design processes as applied on surface
roughness.
Approaches to Experimentation
What is Design of Experiments
Definition of DOE
Why DOE
History of DOE
Basic DOE Example
Factors, Levels, Responses
General Model of Process or System
Interaction, Randomization, Blocking, Replication
Experiment Design Process
Types of DOE
One factorial
Two factorial
Fractional factorial
Screening experiments
Calculation of Alias
DOE Selection Guide
The presentation depicted herein presents briefly an introduction of acceptance sampling along with some major differences amongst the widely used sampling standards.
Acceptance Sampling standards comparison. MIL-STD-105E, MIL-STD-1916, ISO 2859, ISO 3951. About AQLs and OC Curves.
Experimental methods are widely used in industrial settings and research activities. In industrial settings, the main goal is to extract the maximum amount of unbiased information regarding the factors affecting production process form few observations, whereas in research, ANOVA techniques are used to reveal the reality. Drawing inferences from the experimental result is an important step in design process of product. Therefore, proper planning of experimentation is the precondition for accurate conclusion drawn from the experimental findings. Design of experiment is powerful statistical tool introduced by R.A. Fisher in England in the early 1920 to study the effect of different parameters affecting the mean and variance of a process performance characteristics
Taguchi's orthogonal arrays are highly fractional orthogonal designs. These designs can be used to estimate main effects using only a few experimental runs.
Consider the L4 array shown in the next Figure. The L4 array is denoted as L4(2^3).
L4 means the array requires 4 runs. 2^3 indicates that the design estimates up to three main effects at 2 levels each. The L4 array can be used to estimate three main effects using four runs provided that the twthree-factoro factor and three factor interactions can be ignored.
According to WHO (World Health Organization):
“QA is the activity of providing evidence needed to establish confidence among all concerned that quality related activities are being performed effectively.”
According to ISO:
“All those planned and systematic activities implemented to provide adequate confidence that an entity will fulfill requirements for quality.
Design of Experiment (DOE): Taguchi Method and Full Factorial Design in Surfa...Ahmad Syafiq
Taguchi and full factorial design techniques to highlight the application and to compare the effectiveness of the Taguchi and full factorial design processes as applied on surface
roughness.
FDA’s emphasis on quality by design began with the recognition that increased testing does not improve product quality (this has long been recognized in other industries).In order for quality to increase, it must be built into the product. To do this requires understanding how formulation and manufacturing process variables influence product quality.Quality by Design (QbD) is a systematic approach to pharmaceutical development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management.
This presentation - Part VI in the series- deals with the concepts of Design of Experiments. This presentation was compiled from material freely available from FDA , ICH , EMEA and other free resources on the world wide web.
LeanUX (lean user experience) experimentation has mostly focused on "A/B" testing. This presentation reviews how full and half factorial design of experiments might be used in Lean User Experience design.
Exploring Best Practises in Design of Experiments: A Data Driven Approach to ...JMP software from SAS
Learn about best practises in the
design of experiments and a data-driven approach to DOE that increases robustness, efficiency and effectiveness. This was presented at a JMP seminar in the UK.
Heuristic design of experiments w meta gradient searchGreg Makowski
Once you have started learning about predictive algorithms, and the basic knowledge discovery in databases process, what is the next level of detail to learn for a consulting project?
* Give examples of the many model training parameters
* Track results in a "model notebook"
* Use a model metric that combines both accuracy and generalization to rank models
* How to strategically search over the model training parameters - use a gradient descent approach
* One way to describe an arbitrarily complex predictive system is by using sensitivity analysis
These slides provide an overview of the basics of design of experiments. They also describe and give examples of categorical and continuous factors and responses, discrete numeric and mixture variables, and blocking factors. The slides were presented live and in recorded videos as part of the Mastering JMP webcast series. Watch the webcasts at http://www.jmp.com/mastering
Tailor welding blanks generally used for making doors of an automobile get crack along welding line. This leads to rejection and Wrong body production. Its analysis & countermeasure shared in sides.
Challenges Related to Measuring and Reporting Temperature-Dependent Apparent ...RDH Building Science
In North America, the apparent thermal conductivity (and R-value) of building insulation materials is commonly reported at a mean temperature of 24°C (75°F) and practitioners typically assume thermal properties remain constant over the range of temperatures that are experienced in building applications. Researchers have long known and acknowledged the fact that the thermal properties of most building insulation materials change with temperature. There has been little more than academic reason to measure and report this effect. However, interest in temperature-dependent thermal performance has grown with the introduction of new materials, increasing concerns regarding energy performance, and the development of tools transient energy, thermal, and hygrothermal simulation software packages (e.g. Energy Plus, HEAT2, WUFI etc.) that have capacity to account for temperature-dependence. Continue reading by clicking the Download link to the left.
Presented at the 15th Canadian Conference on Building Science and Technology.
six sigma DMAIC approach for reducing quality defects of camshaft binding pro...Niranjana B
Data collection for 11 months revealed that 26% of the defects are due to improper camshaft binding. The six sigma approach involves DMAIC approach with statistical tools involved in each stage. The main root are identified and improvements are implemented. The quality is improved by reducing the number of defects
DESIGN IMPROVISATION OF ELECTROMECHANICAL ACTUATORS FOR OPERATION AT SUB ZERO...ijmech
Control surface actuators are the key systems in any flight vehicle for enabling a strict control on the flight
parameters. The electromechanical actuator developed for an Unmanned aerial vehicle (UAV) is subjected
to sub-zero temperatures due to the altitude of operation. This paper discusses on how an actuator
developed is studied experimentally and improvised in design to ensure performance at -40oC. The
experimental observations are reasoned and supported by theoretical studies and remedial measures
incorporated to improve the actuator performance.
An ESD Case Study with High Speed Interface in Electronics Manufacturing and ...James Tsan
A networking semiconductor component with 25 Gbps high-speed interface experienced high
manufacturing failure rate with CDM-like failure signature at contract manufacturer; design of experiment was performed and ESD source was located. Problem details, solution, future challenge and industry awareness are discussed in this paper.
A Silicon-to-System Thermo-Mechanical Review of ElectronicsKamal Karimanal
A Silicon-to-System Review of Thermo-Mechanical Considerations in Electronics
Author: Kamal Karimanal, Cielution LLC
Thermal and Mechanical challenges to IC package reliability has been addressed with a sufficiently working system of information exchange across a supply chain that spans the foundries to system level assembly plants.The never ending market demand for miniaturization, performance, functionality and cost reduction invariably translates to manufacturing, design, assembly, and reliability challenges to the engineer. Within the Thermal and Mechanical realm these challenges manifest to the engineer in the form of seemingly disconnected problems areas such as BEOL yield, flipchip interconnect reliability, warpage mitigation, heat sink retention design, interface choice, thermally aware board and chassis layout, fan sizing and system level optimization. Evolving technology also introduces newer puzzles such as heterogeneous packaging using 3D ICs. The talk will focus on the tools, methodologies and information exchange protocols used by the thermal management and mechanical reliability professionals across the supply chain to address the various challenges.
Ever been troubled by the blinking sign and didn’t know what to do?
Here’s a handy guide to dashboard symbols so that you’ll never be confused again!
Save them for later and save the trouble!
Symptoms like intermittent starting and key recognition errors signal potential problems with your Mercedes’ EIS. Use diagnostic steps like error code checks and spare key tests. Professional diagnosis and solutions like EIS replacement ensure safe driving. Consult a qualified technician for accurate diagnosis and repair.
Comprehensive program for Agricultural Finance, the Automotive Sector, and Empowerment . We will define the full scope and provide a detailed two-week plan for identifying strategic partners in each area within Limpopo, including target areas.:
1. Agricultural : Supporting Primary and Secondary Agriculture
• Scope: Provide support solutions to enhance agricultural productivity and sustainability.
• Target Areas: Polokwane, Tzaneen, Thohoyandou, Makhado, and Giyani.
2. Automotive Sector: Partnerships with Mechanics and Panel Beater Shops
• Scope: Develop collaborations with automotive service providers to improve service quality and business operations.
• Target Areas: Polokwane, Lephalale, Mokopane, Phalaborwa, and Bela-Bela.
3. Empowerment : Focusing on Women Empowerment
• Scope: Provide business support support and training to women-owned businesses, promoting economic inclusion.
• Target Areas: Polokwane, Thohoyandou, Musina, Burgersfort, and Louis Trichardt.
We will also prioritize Industrial Economic Zone areas and their priorities.
Sign up on https://profilesmes.online/welcome/
To be eligible:
1. You must have a registered business and operate in Limpopo
2. Generate revenue
3. Sectors : Agriculture ( primary and secondary) and Automative
Women and Youth are encouraged to apply even if you don't fall in those sectors.
5 Warning Signs Your BMW's Intelligent Battery Sensor Needs AttentionBertini's German Motors
IBS monitors and manages your BMW’s battery performance. If it malfunctions, you will have to deal with an array of electrical issues in your vehicle. Recognize warning signs like dimming headlights, frequent battery replacements, and electrical malfunctions to address potential IBS issues promptly.
Your VW's camshaft position sensor is crucial for engine performance. Signs of failure include engine misfires, difficulty starting, stalling at low speeds, reduced fuel efficiency, and the check engine light. Prompt inspection and replacement can prevent further damage and keep your VW running smoothly.
What Could Cause The Headlights On Your Porsche 911 To Stop WorkingLancer Service
Discover why your Porsche 911 headlights might flicker out unexpectedly. From aging bulbs to electrical gremlins and moisture mishaps, we're delving into the reasons behind the blackout. Stay tuned to illuminate the road ahead and ensure your lights shine bright for safer journeys.
Things to remember while upgrading the brakes of your carjennifermiller8137
Upgrading the brakes of your car? Keep these things in mind before doing so. Additionally, start using an OBD 2 GPS tracker so that you never miss a vehicle maintenance appointment. On top of this, a car GPS tracker will also let you master good driving habits that will let you increase the operational life of your car’s brakes.
What Are The Immediate Steps To Take When The VW Temperature Light Starts Fla...Import Motorworks
Learn how to respond when the red temperature light flashes in your VW with this presentation. From checking coolant levels to seeking professional help, follow these steps promptly to prevent engine damage and ensure safety on the road.
The Octavia range embodies the design trend of the Škoda brand: a fusion of
aesthetics, safety and practicality. Whether you see the car as a whole or step
closer and explore its unique features, the Octavia range radiates with the
harmony of functionality and emotion
"Trans Failsafe Prog" on your BMW X5 indicates potential transmission issues requiring immediate action. This safety feature activates in response to abnormalities like low fluid levels, leaks, faulty sensors, electrical or mechanical failures, and overheating.
Fleet management these days is next to impossible without connected vehicle solutions. Why? Well, fleet trackers and accompanying connected vehicle management solutions tend to offer quite a few hard-to-ignore benefits to fleet managers and businesses alike. Let’s check them out!
What Does the PARKTRONIC Inoperative, See Owner's Manual Message Mean for You...Autohaus Service and Sales
Learn what "PARKTRONIC Inoperative, See Owner's Manual" means for your Mercedes-Benz. This message indicates a malfunction in the parking assistance system, potentially due to sensor issues or electrical faults. Prompt attention is crucial to ensure safety and functionality. Follow steps outlined for diagnosis and repair in the owner's manual.
In this presentation, we have discussed a very important feature of BMW X5 cars… the Comfort Access. Things that can significantly limit its functionality. And things that you can try to restore the functionality of such a convenient feature of your vehicle.
Why Is Your BMW X3 Hood Not Responding To Release CommandsDart Auto
Experiencing difficulty opening your BMW X3's hood? This guide explores potential issues like mechanical obstruction, hood release mechanism failure, electrical problems, and emergency release malfunctions. Troubleshooting tips include basic checks, clearing obstructions, applying pressure, and using the emergency release.
3. QC STORY
3PREPARED BY K.KARTHIKEYAN
DOE
1.1 Problem Statement
High flash fail rejections in manufacturing line
1.0 PROBLEM SELECTION
What is Flash fail rejections?
It is the insulation failure between armature core and
copper wire coil wound on the armature core. This is
being checked in manufacturing line.
4. QC STORY
4PREPARED BY K.KARTHIKEYAN
DOE
1.2 Why this Problem is important ?
Flash fail rejection is top in Pareto in manufacturing line
rejection.
This will resulting increase scrap value and if not detected
during testing stage can results customer line rejection and
warranty return.
1.3 Theme
To Reduce the manufacturing line rejection.
1.4 Target
Elimination of flash fail rejection in armature before Wk No.15.
1.0 PROBLEM SELECTION
5. QC STORY
5PREPARED BY K.KARTHIKEYAN
DOE
1.5 Action Plan
STEP
P
A
P
A
P
A
P
A
P
A
P
A
P
A
Planned - Actual -
WK NO.15WK NO.13
Problem selection
Observation
WK NO.14WK NO.10 WK NO.11 WK NO.12
Conclusion
Analysis
Action
Check
Standardisation
1.0 PROBLEM SELECTION
6. QC STORY
PREPARED BY K.KARTHIKEYAN 6
DOE
0.194 0.182 0.192 0.186 0.188 0.194
0.143 0.145
0.135 0.137 0.143 0.141
0
0.05
0.1
0.15
0.2
0.25
WK4 WK5 WK6 WK7 WK8 WK9
%Rejection
Week
2.0 OBSERVATION
Armature average rejection – 0.19 %
Average Flash fail rejection – 0.14 %
Average manufacturing line rejection was 1900 ppm ( From week
No.4 to 9) and Flash fail alone average rejection was 1400 ppm.
7. QC STORY
PREPARED BY K.KARTHIKEYAN 7
DOE
MANUFACTURING REJECTION - PARETO
2.0 OBSERVATION
8. QC STORY
PREPARED BY K.KARTHIKEYAN 8
DOE
3.0 ANALYSIS :
3.1 Defective analysis - Concentration chart:
The study of defectives shown all flash fail are due to defective powder coating and
wire touching core. A concentration chart diagram of defective shows no
concentration of defects
3.0 ANALYSIS
3.2 Comparing of Good & Bad sample:
In all bad samples the coating thickness found less than specification of 300 - 400
microns
9. QC STORY
9PREPARED BY K.KARTHIKEYAN
DOE
Gap between
Coil & Armature
3.3 CAUSE AND EFFECT DIAGRAM
A cause & effect diagram was constructed depicting the various probable causes.
Powder
Coating
thickness
Variation
MAN MATERIAL
Sop not adequate
OD clean belt
MACHINEMETHOD
Pre Heating
Temp
Lumps in Powder
Air Knife pressure
Component
orientation Process parameter
Masking JIG
MEASUREMENT
Coating Thickness
not Checked
properly
Not given in drawing
Humidity in powder
Jig not properly seated
Lack of training
Voltage Coater Pr
Uneven fluidization
Program selection
Cleaning method
Powder Storage
level
Cu Plate
Curing
temp
ENVIRONMENT
Humidity
Powder Storage
Room temp
Untrained
Operator
Fatigue
Shell life completed
Rust on Core
Conveyor
Speed
3.0 ANALYSIS
10. QC STORY
10PREPARED BY K.KARTHIKEYAN
DOE
1. Objective of the experiment
2. Selection of factors, levels and expected interactions
3. Selection of experimental design
4. Experimental preparation and randomize the Experimental run
5. Statistical data analysis
6. Experimental Conclusions and recommendations
4.0 STEPS FOR DESIGN OF EXPERIMENT
• ALL THE POSSIBLE CAUSES ARE INVALID. IT IS VERY DIFFCULT TO CHECK
PROBABLE CAUSE HENCE WE DECIDED TO DO DOE TO OPTIMIZE PROCESS
PARAMETER IN POWDER COATING PROCESS BY USING TAGUCHI METHOD.
3.0 ANALYSIS
11. QC STORY
11PREPARED BY K.KARTHIKEYAN
DOE
The Powder Coating process having the following 14 process parameters
2.0 Selection of factors and expected interactions and Response
DESIGN OF EXPERIMENT
To optimize the Powder Coating process parameters through Taguchi method.
1.0 Objective of the experiment:
Sl.No Parameter
1 Conveyor Speed (Hz)
2 Electrostatic Voltage (kV)
3 Sub coater pressure (Mpa)
4 Coater Pressure (Mpa)
5 Powder feeder Pressure (Mpa)
6 Air hopper Pressure(Mpa)
7 OD removal belt height (mm)
8 Air Knife 1 (Mpa)
9 Air Knife 2 (Mpa)
10 Air Knife 3 (Mpa)
11 Air Knife 4 (Mpa)
12 Air Knife 5 (Mpa)
13 Pre heating temperature (o
C)
14 Curing temperature (o
C)
Sl.No Parameter
1 Conveyor Speed
2 Electrostatic Voltage
3
Preheating
Temperature
4 Curing Temperature
5 Coater Pressure
Our team has selected all five key
process parameter to optimize the
powder coating process based on
the Experience and knowledge.
12. QC STORY
12PREPARED BY K.KARTHIKEYAN
DOE
A. Conveyor Speed in Hz ( 15 Hz)
B. Electrostatic voltage in kV (60 kV)
C. Preheating temperature in O C (150O c)
D. Curing temperature in O C ( 240O c )
E. Coater pressure in bar ( 0.05 bar)
2.a Choice of Main Factors
2.b. Interaction of Interest:
1. Conveyor speed & Coater pressure (A & E)
2. Electrostatic voltage & Coater pressure (B & E)
3. Preheating temp & Coater pressure (C & E)
4. Curing temp & Coater pressure (D & E)
DESIGN OF EXPERIMENT
13. QC STORY
13PREPARED BY K.KARTHIKEYAN
DOE
2.c.Choice of factor levels
Three levels selected for each factors based on experience & knowledge
Level 1 , Level 2 , Level 3
Replication = 5 Nos
Factors Level 1 Level 2 Level 3
Conveyor speed (Hz) 15 16 17
Electrostatic Voltage (kV) 50 55 60
Preheating temperature ('C) 150 180 210
Curing temperature ('C) 240 280 320
Coater Pressure (bar) 0.03 0.05 0.08
Factors Specfication
Existing
Setting
Conveyor speed (Hz) 15 15
Electrostatic Voltage
(kV)
55 ±5 60
Preheating temperature
('C)
180±20°C 150
Curing temperature ('C) 280±40°C 240
Coater Pressure (bar) 0.05±0.02 0.05
2.d. Selection of Response
Coating thickness in Armature ( Spec: 300 – 400 microns)
DESIGN OF EXPERIMENT
14. QC STORY
14PREPARED BY K.KARTHIKEYAN
DOE
Total no of Factors 5 and 4 Interactions with 3 levels
3.1. Required Degree of freedom for main factor
= (No of levels - 1) X No of factors = (3 - 1) X 5 = 10
3.2. Required Degree of freedom for Interaction
= ((DOF of A X DOF of E) + (DOF of B X DOF of E)
+ (DOF of C X DOF of E) +( DOF of D X DOF of E) )
= ((3 - 1) X(3 – 1))+((3 - 1) X (3 - 1))+((3 - 1) X (3 - 1) )+( (3 - 1) X (3 - 1))
= 16
3. Selection of Experimental Design:
DESIGN OF EXPERIMENT
15. QC STORY
15PREPARED BY K.KARTHIKEYAN
DOE
3.3.Total Degrees of freedom = DOF of Main effect + DOF of Interaction
= 10 + 16
= 26
3.4.Minimum no of Experiments = Total Degrees of Freedom + 1
= 26 + 1
= 27
3.5.Suitable Orthogonal Array
from Table
= L
27
(3) 13
DESIGN OF EXPERIMENT
16. QC STORY
16PREPARED BY K.KARTHIKEYAN
DOE
3.6. Required Linear graph:
3.7. Standard Linear graph (Select from orthogonal table):
A
E B
C
D
B X E
A X E
D X E
C X E
1
5 2
9
10
7
6
118
3
124
13
1
5 2
9
10
7
6
118
3
124
13
DESIGN OF EXPERIMENT
17. QC STORY
17PREPARED BY K.KARTHIKEYAN
DOE
3.8. Modified Standard Linear graph
Assignment of factors is done using the Linear graph
Nodes – factors
Lines – interaction between factors
1
(A)
2
(B)
9
(C)
10
(D)
6 7 (AXE)
8 11 (BXE)
4 12 (DXE)
3 13 (CXE)
5
(E)
DESIGN OF EXPERIMENT
18. QC STORY
18PREPARED BY K.KARTHIKEYAN
DOE
3.9. Draw Design Layout of Experiment: (From OA table) = L27 Array
DESIGN OF EXPERIMENT
19. QC STORY
19PREPARED BY K.KARTHIKEYAN
DOE
FACTORS AND LEVELS FOR THE EXPERIMENTS
1. A – Conveyor Speed in HZ
2. B – Electrostatic Voltage kV
9. C - Preheating Temperature °C
10. D - Curing Temperature °C
5. E – Coater Pressure in bar
6 7. Conveyor Speed in HZ X Coater Pressure in bar ( AXE)
8 11. Electrostatic Voltage kV X Coater Pressure in bar (BXE)
3 13. Preheating Temperature °C X Coater Pressure in bar (CXE)
4 12. Curing Temperature °C X Coater Pressure in bar (DXE)
DESIGN OF EXPERIMENT
20. QC STORY
20PREPARED BY K.KARTHIKEYAN
DOE
3.10 Physical Layout of Experimentation:
DESIGN OF EXPERIMENT
21. QC STORY
21PREPARED BY K.KARTHIKEYAN
DOE
4. 0 Experimental run Result:
DESIGN OF EXPERIMENT
22. QC STORY
22PREPARED BY K.KARTHIKEYAN
DOE
171615
340
330
320
605550 210180150
320280240
340
330
320
0.080.050.03
C onv ey or speed
MeanofMeans
Electrostatic v oltage Preheating temp
C uring Temp C oater pressure
Main Effects Plot for Means
Data Means
Interpretation
A2,B1,C2,D2, & E1
are best levels
AVERAGE RESPONSE GRAPHS OF MAIN EFFECTS
Interpretation of Experimental trials
5.0 Analysis and Interpretation of Experimental trials:
23. QC STORY
23PREPARED BY K.KARTHIKEYAN
DOE
AVERAGE RESPONSE GRAPHS OF INTERACTION EFFECTS
Interpretation : Electrostatic Voltage x Coater pressure, Preheating Temperature x
Coater pressure, Curing Temperature x Coater pressure interactions are exists
350
330
310
605550 320280240
350
330
310
0.080.050.03
350
330
310
350
330
310
171615
350
330
310
210180150
Conveyor speed
Electrostatic voltage
Preheating temp
Curing Temp
Coater pressure
15
16
17
speed
Conveyor
50
55
60
voltage
Electrostatic
150
180
210
temp
Preheating
240
280
320
Temp
Curing
0.03
0.05
0.08
pressure
Coater
Interaction Plot for Means
Data Means
Interpretation of Experimental trials
24. QC STORY
24PREPARED BY K.KARTHIKEYAN
DOE
SELECTION OF OPTIMUM COMBINATION (BASED ON MAIN EFFECT
PLOT AND INTERACTION EFFECT PLOT)
The best combination is
A2 B1 C2 D2 & E1
The best levels of individual factors are
Factors Level 1 Level 2 Level 3
Conveyor speed (Hz) 15 16 17
Electrostatic Voltage (kV) 50 55 60
Preheating temperature ('C) 150 180 210
Curing temperature ('C) 240 280 320
Coater Pressure (bar) 0.03 0.05 0.08
Interpretation of Experimental trials
25. QC STORY
25PREPARED BY K.KARTHIKEYAN
DOE
If the ‘F Calculated ’ value is greater than ‘ F Table’ value or p value
is less tan 0.05 then that factor shall be considered as significant.
If the ‘F Calculated ’ value is greater than ‘ F Table’ value or p value
is less tan 0.05 then that factor shall be considered as significant.
INTERPRETATIONINTERPRETATION
5. Interpretation through ANOVA Method:
DOF
Sum of
Square
MSS F cal F Table P value % Cont
Result
(Fcal > F Tab
2 65.80 32.90 0.152 3.080 0.859 0.1 Not Significant
2 14062.40 7031.20 32.425 3.080 0.000 25.5 Significant
2 557.60 278.80 1.286 3.080 0.281 1.0 Not Significant
2 6355.70 3177.85 14.655 3.080 0.000 11.5 Significant
2 1630.90 815.45 3.761 3.080 0.026 3.0 Significant
4 2247.20 561.80 2.591 2.455 0.041 4.1 Significant
4 817.90 204.48 0.943 2.455 0.442 1.5 Not Significant
4 3747.00 936.75 4.320 2.455 0.003 6.8 Significant
4 2321.100 580.275 2.676 2.455 0.036 4.2 Significant
108 23419.00 216.84 42.4
134 55225.00 100
Error
Total
Curing temp x coater pressure
(DXE)
Conveyor speed x coater pressure
(AXE)
Electrostatic voltage x coater
pressure (BXE)
Coater Pressure bar (E)
Conveyor speed - Hz (A)
Electrostatic voltage Kv -(B)
Preheating temp x Coater
pressure (CXE)
Factors
Preheating temperature °C ('C)
Curing temperature °C (D)
Interpretation through ANOVA
26. QC STORY
26PREPARED BY K.KARTHIKEYAN
DOE
INFERENCE ON ANOVA:
Interpretation Of ANOVA Based On P-Value
Based on the P Value from the ANOVA table the following are the inferences. The
details Of Individual Factor Significance And Significance of interaction were given
below.
Factors Level 1 Level 2 Level 3
A Conveyor speed (Hz) 15 16 17
B Electrostatic Voltage (kV) 50 55 60
C Preheating temperature ('C) 150 180 210
D Curing temperature ('C) 240 280 320
E Coater Pressure (bar) 0.03 0.05 0.08
1. Factor A - Insignificant
2. Factor B - Significant
3. Factor C - Insignificant
4. Factor D - Significant
5. Factor E - Significant
6. InteractionA X E - Significant
7. Interaction B X E - Insignificant
8. Interaction C X E - Significant
9. Interaction D X E - Significant
Interpretation through ANOVA
27. QC STORY
27PREPARED BY K.KARTHIKEYAN
DOE 4.0 ACTION
Factors
Previous
setting
Optimized
setting
A Conveyor speed in rpm 15 16
B Electrostatic voltage in kV 60 50
C Pre heat temperature in o C 150 180
D Curing temperature in o C 240 280
E Coater pressure in bar 0.05 0.03
From the Experiment the Armature powder coating process
parameters are optimized.
RECOMMENDED (OPTIMISED) PRODUCTION SETTING
28. QC STORY
28PREPARED BY K.KARTHIKEYAN
DOE
Based on the best optimum combination obtained from the results
of experiments a confirmatory run has been done and the results
were verified with the predicted values and found well near to the
values.
30 Nos.of samples taken for the optimum level and Cpk values
were recorded and the same values were interpreted.
CONFIRMATORY TRIAL WITH PREDICTED VALUES
4.0 ACTION
29. QC STORY
29PREPARED BY K.KARTHIKEYAN
DOE
CONFIRMATORY TRIAL
5.0 CHECK
Before DOE : 1.07 After DOE : 1.27
30. QC STORY
PREPARED BY K.KARTHIKEYAN 30
DOE
REJECTION TREND (After Improvements)
5.0 CHECK
6.0 STANDARDISATION
31. QC STORY
31PREPARED BY K.KARTHIKEYAN
DOE
> From the Design of Experiment analysis, powder
coating process parameter is optimized as per the
below process setting and it is clearly indicates that
the Powder coating process is well within the process
center.
7.0 CONCLUSION
> Thus by improving the Cpk of Powder coating process
Armature manufacturing line rejection reduced from 1900 PPM
to 700 PPM. Flash fail rejections reduced from 1400 PPM to 200
PPM