This document summarizes a report on developing a total cost model for additive manufacturing (AM), also known as 3D printing. It conducted experiments using laser sintering (LS) and selective laser melting (SLM) to understand build failure rates, manual post-processing costs, and how costs decrease with higher machine utilization. The results provide a methodology to analyze the full economic costs of AM and understand when it may have a viable business case compared to traditional manufacturing.
150610 Exploring the supply chain opportunities of additive manufacturing in ...SINTAS
150610 Exploring the supply chain opportunities of additive manufacturing in aviation’s spare part industry (Gereon Deppe)
RapidTech meeting 10-06-2015
This is a presentation I made for the Pacific Design and Manufacturing conference in February, 2014. There were three presentations and this was the first one. It takes a look at some of the new materials in 3D Printing.
150610 Exploring the supply chain opportunities of additive manufacturing in ...SINTAS
150610 Exploring the supply chain opportunities of additive manufacturing in aviation’s spare part industry (Gereon Deppe)
RapidTech meeting 10-06-2015
This is a presentation I made for the Pacific Design and Manufacturing conference in February, 2014. There were three presentations and this was the first one. It takes a look at some of the new materials in 3D Printing.
On July 10th Innovate UK and the KTN held a business innovation day to showcase 30 of the Innovate UK projects that are currently active in the area of Additive Manufacturing. The presentations and pitches made on the day are now available to download. Topic 3 focuses on Post Processing
FDM Modeled Polymer Tooling for Plastic Injection MoldingIJAMSE Journal
Rapid Prototyping is being accepted globally by industries for its potential in saving on process time and cost. Rapid Tooling helps Rapid Prototyping grow beyond its conventional Feel & Fit status to Feel Fit Function status and is increasingly becoming popular. However, potential of rapid prototyping for normal
production run is still not being realized. In that situation Rapid Tooling becomes a viable alternative. The greatest opportunity for rapid tooling implementation is the use of Additive Manufacturing (AM) technology. Further, polymer based direct rapid tooling provide large cost reduction and can also be
readily accessible by industries. With the advances in materials along with the new access and low cost plastic based- AM equipment, direct use Polymer Rapid Tools (PRTs) would be a far more advantageous option in creating injection molds for low and highly flexible production. However, the use of polymer
based direct rapid tooling by industries is curtailed due to the issues with the dimensional stability of the polymer based rapid tooling molds. Apart from dimensional tolerances, there are also issues with the life of these polymer based mold as they wear fast and are also not able to sustain high injection pressures in an Injection molding machine. Another, major problem with the polymer based rapid tooling is the poor thermal conductivity of polymeric materials due to which there is an increase in the cooling time and ultimately leading to decrease in productivity. Therefore, before proposing polymer based rapid tooling as a solution to industries to cut down the product development time and bring down the costs, a thorough study of the issues related to the same is imperative. This paper investigates the dimensional accuracy of striker component produced by ABS mold inserts. For dimensional accuracy a reverse engineering technique3D scanning is used which is compared with CAD file and inspected with COMET plus software. Further, the outputs are validated with vernier caliper. The mold insert is manufactured by Fused Deposition Modeling (FDM) technology which is used on injection molding machine
Design and Development of Honeycomb Structure for Additive Manufacturingijtsrd
the demand for shorter product development time has resulted in the introduction of a new paradigm called Additive Manufacturing AM . Due to its significant advantages in terms of cost effective, lesser build time, elimination of expensive tooling, design flexibility AM is finding applications in many diverse fields of the industry today. One of the recent applications of this technology is for fabrication of cellular structures. Cellular structures are designed to have material where it is needed for specific applications. Compared to solid materials, these structures can provide high strength-to-weight ratio, good energy absorption characteristics and good thermal and acoustic insulation properties to aerospace, medical and engineering products. However, due to inclusion of too many design variables, the design process of these structures is a challenge task. Furthermore, polymer additive manufacturing techniques, such as fused deposition modeling FDM process which shows the great capability to fabricate these structures, are still facing certain process limitations in terms of support structure requirement for certain category of cellular structures. Therefore, in this research, a computer-aided design CAD based method is proposed to design and develop hexagonal honeycomb structure self-supporting periodic cellular structure for FDM process. This novel methodology is found to have potential to create honeycomb cellular structures with different volume fractions successfully without any part distortion. Once designing process is complete, mechanical and microstructure properties of these structures are characterized to investigate effect of volume fraction on compressive strength of the part. Volume fraction can be defined as the volume percentage of the solid material inside the cellular structure and it is varied in this thesis by changing the cell size and wall thickness of honeycombs. Compression strength of the honeycomb structure is observed to increase with the increase in the volume fraction and this behavior is compared with an existing Wierzbicki expression, developed for predicting compression properties. Some differences are noticed in between experimentally tested and Wierzbicki model estimated compressive strength. These differences may be attributed to layer by layer deposition strategy and the residual stress inherent to the FDM-manufacturing process. Narendra Kumar Rajak | Prof. Amit Kaimkuriya "Design and Development of Honeycomb Structure for Additive Manufacturing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18856.pdf
The impact of additive manufacturing on micro reactor technology (slideshare ...Raf Reintjens
The continuous tubular reactor is a well-known concept which is applied broadly and has proven its value to the chemical industry. In essence the micro reactor is nothing else than a tubular reactor with an unusual small diameter. Its excellent performance originates from the fact that the characteristic time for heat and mass transfer scales quadratic with the length scale. Ten times smaller diameter results in a hundred times faster transfer.
But, the very principles that lead to high performance seem to disable economical viable applications. Even at ‘micro reactor level’ productivity an astronomically large number of parallel channels is required to reach plant scale production capacities. The negative influence on manufacturability and cost can be countered by influencing the fluid dynamics inside the channel. Making use of secondary flow phenomena we succeed to maintain the ‘micro reactor level’ productivity at mm sized channel diameters. The desired secondary flow effect originates from influencing the shape, geometry and lay-out of the channel.
Selective laser melting (3D metal printing) is a new fast developing manufacturing technology that delivers excellent freedom of design combined with a promising cost level. Those properties match very well with the needs within micro reactor technology, and act as a strong enabler for applications in process development as well as industrial production.
Integrating parametric design with robotic additive manufacturing for 3D clay...Antonio Arcadu
ABSTRACT This paper presents an ongoing work in relation to the development of a parametric design algorithm and an automated system for additive manufacturing that aims to be implemented in 3D clay printing tasks. The purpose of this experimental study is to establish a first insight and provide information as well as guidelines for a comprehensive and robust additive manufacturing methodology that can be implemented in the area of 3D clay printing, aiming to be widely available and open for use in the relevant construction industry. Specifically, this paper emphasizes on the installation of an industrial extruder for 3D clay printing mounted on a robot, on toolpath planning process using a parametric design environment and on robotic execution of selected case studies. Based on existing 3D printing technology principles and on available rapid prototyping mechanisms, this process suggests an algorithm for system’s control as well as for robotic toolpath development applied in additive manufacturing of small to medium objects. The algorithm is developed in a parametric associative environment allowing its flexible use and execution in a number of case studies, aiming to tentatively test the effectiveness of the suggested robotic additive manufacturing workflow and their future implementation in large scale examples.
Autors: O. Kontovourkisa and G. Tryfonos
Additive manufacturing (AM) or additive layer manufacturing (ALM) is the industrial production name for 3D printing, a computer controlled process that creates three dimensional objects by depositing materials, usually in layers.
AM is a rapidly growing field that is having an impact on multiple industries by simplifying the process to go from a 3D model to a finished product.
In contrast to conventional manufacturing processes, AM fabricates objects by adding materials as required which eliminates the necessity of subtracting materials (by means of machining, milling, carving, etc.) to obtain desired shapes.
AM can advantageously fabricate complex geometries with no part-specific tooling and much less waste material.
In the construction sector, architectural models have been created with AM methods for more than a decade.
Recent years have seen a vast increase in research on printing methods for building components.
AM allows building companies to produce geometrically complex structures, to vary materials within a component according to its functions, and to automate the construction process starting from a digital model.
The technology can bring significant benefits to the construction industry in terms of increased customization, reduced construction time, reduced manpower, and construction cost.
On July 10th Innovate UK and the KTN held a business innovation day to showcase 30 of the Innovate UK projects that are currently active in the area of Additive Manufacturing. The presentations and pitches made on the day are now available to download. Topic 3 focuses on Post Processing
FDM Modeled Polymer Tooling for Plastic Injection MoldingIJAMSE Journal
Rapid Prototyping is being accepted globally by industries for its potential in saving on process time and cost. Rapid Tooling helps Rapid Prototyping grow beyond its conventional Feel & Fit status to Feel Fit Function status and is increasingly becoming popular. However, potential of rapid prototyping for normal
production run is still not being realized. In that situation Rapid Tooling becomes a viable alternative. The greatest opportunity for rapid tooling implementation is the use of Additive Manufacturing (AM) technology. Further, polymer based direct rapid tooling provide large cost reduction and can also be
readily accessible by industries. With the advances in materials along with the new access and low cost plastic based- AM equipment, direct use Polymer Rapid Tools (PRTs) would be a far more advantageous option in creating injection molds for low and highly flexible production. However, the use of polymer
based direct rapid tooling by industries is curtailed due to the issues with the dimensional stability of the polymer based rapid tooling molds. Apart from dimensional tolerances, there are also issues with the life of these polymer based mold as they wear fast and are also not able to sustain high injection pressures in an Injection molding machine. Another, major problem with the polymer based rapid tooling is the poor thermal conductivity of polymeric materials due to which there is an increase in the cooling time and ultimately leading to decrease in productivity. Therefore, before proposing polymer based rapid tooling as a solution to industries to cut down the product development time and bring down the costs, a thorough study of the issues related to the same is imperative. This paper investigates the dimensional accuracy of striker component produced by ABS mold inserts. For dimensional accuracy a reverse engineering technique3D scanning is used which is compared with CAD file and inspected with COMET plus software. Further, the outputs are validated with vernier caliper. The mold insert is manufactured by Fused Deposition Modeling (FDM) technology which is used on injection molding machine
Design and Development of Honeycomb Structure for Additive Manufacturingijtsrd
the demand for shorter product development time has resulted in the introduction of a new paradigm called Additive Manufacturing AM . Due to its significant advantages in terms of cost effective, lesser build time, elimination of expensive tooling, design flexibility AM is finding applications in many diverse fields of the industry today. One of the recent applications of this technology is for fabrication of cellular structures. Cellular structures are designed to have material where it is needed for specific applications. Compared to solid materials, these structures can provide high strength-to-weight ratio, good energy absorption characteristics and good thermal and acoustic insulation properties to aerospace, medical and engineering products. However, due to inclusion of too many design variables, the design process of these structures is a challenge task. Furthermore, polymer additive manufacturing techniques, such as fused deposition modeling FDM process which shows the great capability to fabricate these structures, are still facing certain process limitations in terms of support structure requirement for certain category of cellular structures. Therefore, in this research, a computer-aided design CAD based method is proposed to design and develop hexagonal honeycomb structure self-supporting periodic cellular structure for FDM process. This novel methodology is found to have potential to create honeycomb cellular structures with different volume fractions successfully without any part distortion. Once designing process is complete, mechanical and microstructure properties of these structures are characterized to investigate effect of volume fraction on compressive strength of the part. Volume fraction can be defined as the volume percentage of the solid material inside the cellular structure and it is varied in this thesis by changing the cell size and wall thickness of honeycombs. Compression strength of the honeycomb structure is observed to increase with the increase in the volume fraction and this behavior is compared with an existing Wierzbicki expression, developed for predicting compression properties. Some differences are noticed in between experimentally tested and Wierzbicki model estimated compressive strength. These differences may be attributed to layer by layer deposition strategy and the residual stress inherent to the FDM-manufacturing process. Narendra Kumar Rajak | Prof. Amit Kaimkuriya "Design and Development of Honeycomb Structure for Additive Manufacturing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18856.pdf
The impact of additive manufacturing on micro reactor technology (slideshare ...Raf Reintjens
The continuous tubular reactor is a well-known concept which is applied broadly and has proven its value to the chemical industry. In essence the micro reactor is nothing else than a tubular reactor with an unusual small diameter. Its excellent performance originates from the fact that the characteristic time for heat and mass transfer scales quadratic with the length scale. Ten times smaller diameter results in a hundred times faster transfer.
But, the very principles that lead to high performance seem to disable economical viable applications. Even at ‘micro reactor level’ productivity an astronomically large number of parallel channels is required to reach plant scale production capacities. The negative influence on manufacturability and cost can be countered by influencing the fluid dynamics inside the channel. Making use of secondary flow phenomena we succeed to maintain the ‘micro reactor level’ productivity at mm sized channel diameters. The desired secondary flow effect originates from influencing the shape, geometry and lay-out of the channel.
Selective laser melting (3D metal printing) is a new fast developing manufacturing technology that delivers excellent freedom of design combined with a promising cost level. Those properties match very well with the needs within micro reactor technology, and act as a strong enabler for applications in process development as well as industrial production.
Integrating parametric design with robotic additive manufacturing for 3D clay...Antonio Arcadu
ABSTRACT This paper presents an ongoing work in relation to the development of a parametric design algorithm and an automated system for additive manufacturing that aims to be implemented in 3D clay printing tasks. The purpose of this experimental study is to establish a first insight and provide information as well as guidelines for a comprehensive and robust additive manufacturing methodology that can be implemented in the area of 3D clay printing, aiming to be widely available and open for use in the relevant construction industry. Specifically, this paper emphasizes on the installation of an industrial extruder for 3D clay printing mounted on a robot, on toolpath planning process using a parametric design environment and on robotic execution of selected case studies. Based on existing 3D printing technology principles and on available rapid prototyping mechanisms, this process suggests an algorithm for system’s control as well as for robotic toolpath development applied in additive manufacturing of small to medium objects. The algorithm is developed in a parametric associative environment allowing its flexible use and execution in a number of case studies, aiming to tentatively test the effectiveness of the suggested robotic additive manufacturing workflow and their future implementation in large scale examples.
Autors: O. Kontovourkisa and G. Tryfonos
Additive manufacturing (AM) or additive layer manufacturing (ALM) is the industrial production name for 3D printing, a computer controlled process that creates three dimensional objects by depositing materials, usually in layers.
AM is a rapidly growing field that is having an impact on multiple industries by simplifying the process to go from a 3D model to a finished product.
In contrast to conventional manufacturing processes, AM fabricates objects by adding materials as required which eliminates the necessity of subtracting materials (by means of machining, milling, carving, etc.) to obtain desired shapes.
AM can advantageously fabricate complex geometries with no part-specific tooling and much less waste material.
In the construction sector, architectural models have been created with AM methods for more than a decade.
Recent years have seen a vast increase in research on printing methods for building components.
AM allows building companies to produce geometrically complex structures, to vary materials within a component according to its functions, and to automate the construction process starting from a digital model.
The technology can bring significant benefits to the construction industry in terms of increased customization, reduced construction time, reduced manpower, and construction cost.
Printing the Future: From Prototype to ProductionCognizant
Additive manufacturing (AM) such as 3-D printing heralds a new industrial revolution. We offer a framework for analyzing capabilities and implementing AM technologies to help you smoothly move from prototyping to volume production.
You have probably heard of the Lean management concept and its growing popularity in the business world. But don’t worry if you didn’t. In the next few paragraphs, you will get familiar with the Lean methodology.
Actually, there is no surprise that Lean management is now widespread across industries. Thanks to its core values and positive impact on companies’ overall performance, the Lean concept appears to be a universal management tool.
You can apply the concept of Lean in any business or production process, from manufacturing to marketing and software development.
The Lean methodology relies on 3 very simple ideas:
deliver value from your customer’s perspective
eliminate waste (things that don’t bring value to the end product)
continuous improvement
So now, when you know the core idea, let’s dig deeper and get to know the basic principles of Lean management and where it comes from.
What is Lean Management, and How Did It Start?
Before you start with the basic Lean principles, you need to realize that the Lean methodology is about continuously improving work processes, purposes, and people.
Instead of holding total control of work processes and keeping the spotlight, Lean management encourages shared responsibility and shared leadership.
This is why the two main pillars of the Lean methodology are:
Respect for people
Continuous improvements
lean pillars"The Lean Pillars"
After all, a good idea or initiative can be born at any level of the hierarchy, and Lean trusts the people who are doing the job to say how it should be done.
Currently, Lean management is a concept that is widely adopted across various industries. However, it has actually derived from the Toyota Production System, established around 70 years ago.
The Birth of Lean
In the late 1940s, when Toyota put the foundations of Lean manufacturing, they aimed to reduce processes that don’t bring value to the end product.
By doing so, they succeeded in achieving significant improvements in productivity, efficiency, cycle time, and cost-efficiency.
Thanks to this notable impact, Lean thinking has spread across many industries and evolved to 5 basic Lean management principles as described by the Lean Management Institute.
Indeed, the term Lean was made up by John Krafcik (currently CEO of Google’s self-driving car project Waymo) in his 1988 article “Triumph of the Lean Production System”.
Lean software development
In 2003, Mary and Tom Poppendieck published their book “Lean software development: an Agile Toolkit”. The book describes how you can apply the initial principles of the Lean methodology to software development.
At the end of the day, Lean software development comes down to 7 principles. In the beginning, it didn’t gain popularity, but a few years later, it became one of the most popular software development methods.
10 Years Kanban Experience In 1 Free Book:
Project Manager's Guide to Kanban
Short training course as introduction to business case evaluation for the trending technology of additive manufacturing (3D printing) of metal parts. Explanation of main cost drivers, influences and approach of evaluation. Explanation of the right reporting and the use of AM Cost application.
Addresses: product manger, buyers, engineers and mangers in manufacturing industries.
Additive Manufacturing Impact on Supply Chain and Production SchedulingVarun Patel
This is presentation of winner of case study competition conducted by GE at SIOM Nashik. The topic evolved around Additive Manufacturing/3D Printing and its commercial application.
Additive manufacturing 3D Printing technologySTAY CURIOUS
Additive manufacturing 3D Printing
3D printing is the process of building an object one thin layer at a time. It is fundamentally additive rather than subtractive in nature. To many, 3D printing is the singular production of often-ornate objects on a desktop printer.
A parts assembly station on a production line exhibits a severe vibra.pdfshalins6
A parts assembly station on a production line exhibits a severe vibration problem. A simplified
schematic representation is shown in Fig. 4.26. Two large tables of mass m_1 and m_2 are each
mounted to a sliding metal plate on resilient rubber mounts with shear stiffness K_1 and K_2, as
shown. The tables are each subjected to a vibrational excitation force, F_1(t) and F_2(t). The
plates are able to slide viscously on a second pair of deformable rubber mounts, with shear
stiffnesses K_3 and K_4. The viscous sliding coefficients are B_1 and B_2. The two plates are
coupled by a shaft with longitudinal stiffness K_5. Draw a linear graph for the system using the
two forces F_1 and F_2 as inputs.
Solution
An assembly line is a manufacturing process in which interchangeable parts are added to a
product in a sequential manner to create an end product. In most cases, a manufacturing
assembly line is a semi-automated system through which a product moves. At each station along
the line some part of the production process takes place. The workers and machinery used to
produce the item are stationary along the line and the product moves through the cycle, from
start to finish.
Assembly line methods were originally introduced to increase factory productivity and
efficiency. Advances in assembly line methods are made regularly as new and more efficient
ways of achieving the goal of increased throughput (the number of products produced in a given
period of time) are found. While assembly line methods apply primarily to manufacturing
processes, business experts have also been known to apply these principles to other areas of
business, from product development to management.
The introduction of the assembly line to American manufacturing floors in the early part of the
twentieth century fundamentally transformed the character of production facilities and businesses
throughout the nation. Thanks to the assembly line, production periods shortened, equipment
costs accelerated, and labor and management alike endeavored to keep up with the changes.
Today, using modern assembly line methods, manufacturing has become a highly refined process
in which value is added to parts along the line. Increasingly, assembly line manufacturing is
characterized by \"concurrent processes\"—multiple parallel activities that feed into a final
assembly stage. These processes require sophisticated communications systems, material flow
plans, and production schedules. The fact that the assembly line system is a single, large system
means that failures at one point in the \"line\" cause slowdowns and repercussions from that
point forward. Keeping the entire system running smoothly requires a great deal of coordination
between the parts of the system.
Computer power has enabled tracking systems to become more sophisticated and this, in turn,
has made it possible to reduce the costs associated with holding inventories. Just-in-time (JIT)
manufacturing methods have been developed to reduce t.
Design and Engineering Module 3: Prototype to ProductNaseel Ibnu Azeez
As per KTU Syllabus Design and Engineering
Prototyping- rapid prototyping; testing and evaluation of design; Design modifications; Freezing the design; Cost analysis. Engineering the design – From prototype to product.
Planning; Scheduling; Supply chains; inventory; handling; manufacturing/construction operations; storage; packaging; shipping; marketing; feed-back on design.
1. The economics of 3D Printing:
A total cost perspective
Project Report
www.digits2widgets.com
D2W
2. EPSRC Centre for Innovative Manufacturing in Additive Manufacturing
2
The economics of 3DP: A total cost perspective – Project Report
2
Test geometry built with Selective Laser Melting
using stainless steel as a build material
Additive Manufacturing (AM),
also known as 3D Printing, has
captured the imagination of
many technology observers and
manufacturing professionals.
The technology has been widely
heralded as a means to rethink
design, digitise manufacturing,
produce to demand, and
customise products.
While the technological abilities
of AM systems have been
widely discussed, we still lack
a detailed understanding of
the key variables that underpin
the business case of AM. This
project sets out to develop
a total cost model of AM
operations, as a fundamental
precursor to defining viable
business cases for novel,
as well as redistributed,
manufacturing applications.
3. EPSRC Centre for Innovative Manufacturing in Additive Manufacturing
3
The economics of 3DP: A total cost perspective – Project Report
3
Executive summary
by Dr Martin Baumers, Prof Matthias Holweg and Jonathan Rowley
AM processes are generally associated with two
advantages over conventional manufacturing
techniques. Firstly, they avoid many of the tooling-
related constraints on the geometries that can
be achieved through conventional manufacturing
processes. Secondly, AM allows the efficient
creation of products in very low volumes, down
to a single unit, enabling the manufacture of
customised or highly differentiated products.
The technological opportunities that AM presents are
not in question. We do however still lack a fundamental
understanding of the economics that underpin the
application of this technology, which is a fundamental
precursor to developing a business case for its application.
In this report we present the findings of a project aiming to
develop our understanding of the underlying economics
of AM 1
.
It is frequently claimed that the generic advantages
associated with AM will lead to flourishing supply chain
innovation challenging the existing paradigm of centralised
mass manufacturing. However, the successful and
meaningful adoption of AM will depend on a favourable
business case of which, at present, key aspects are not
fully measured and understood. This underlying research
addresses the identified gap.
As a central element for making the business case
towards AM adoption, existing costing approaches have
largely focused on capital investments and consumables,
with an emphasis on build materials. Analyses of such
“well-structured costs” have observed that utilising the
available machine capacity forms a prerequisite for efficient
operations. This is also a core principle of traditional
manufacturing, which is directed at achieving economies
of scale and, as a result, has led to the formation of global
supply chains in many industries.
This stands in contrast to AM, where the underlying
reason for the different requirements towards full utilisation
is that the technology is inherently parallel, allowing the
contemporaneous deposition of multiple geometries.
Moreover, existing analyses of AM resource consumption
have largely ignored hidden or so-called “ill-structured”
costs relating to build failure, part rejection and ancillary
manual processes, such as support removal and surface
finishing. This omission has come at the expense of
industrial applicability, also leading to a lack of realistic
decision tools for the support of AM technology adoption
which are an essential prerequisite for successful diffusion.
Over the duration of this project, we set out to develop
new methodologies and conducted a series of experiments
to build up a body of data supporting a realistic and
comprehensive costing model. Overall, 20 build experiments
were carried out on state-of-the-art polymeric Laser
Sintering (LS) and metallic Selective Laser Melting
(SLM) platforms.
As polymeric LS constitutes one of the most commonly
adopted technologies for the additive manufacture of
end-use components and is capable of delivering useful
material properties, the project has concentrated on LS in its
experimental work. The key methodologies employed in the
analysis of LS, together with reached results, are presented
in this report.
We have identified three aspects that have proven to be
of special significance for the formulation of the total cost
perspective for AM:
• it is known that the unit cost achievable with LS is
dependent on the degree of build volume utilisation.
This relationship underlies the approach taken in
this project;
• AM processes do not operate in isolation. They are
embedded in a sequence of ancillary process steps that
can, as the project has identified, be adequately captured
through process mapping;
• at the current state of technology, AM processes are
prone to build failure events of various sorts, which all
have a detrimental effect on cost and thus need to be
incorporated in any costing model.
We further demonstrate that there is a relationship between
the quantity of parts included within a build volume and
the resulting unit cost. We show that sub-normal machine
utilisation leads to higher unit cost, as one would expect.
We also show that once the process operates at technical
efficiency (optimal build space utilisation) there are no cost
benefits from repeating the build process.
Based on the experimental results we develop a total cost
model that accommodates both manual process inputs
and interventions as well as the risk of build failure. The
methodology developed within this project thus provides
the basis upon which any economic case for AM,
associated network effects, and potentially redistributed
manufacturing can be built.
1
This report is based on the findings of the project “The enabling role of 3D Printing in redistributed manufacturing: a total cost model”,
which was funded through the 3DP-RDM network, and aims to develop a fresh and realistic perspective on the full cost of operating AM
technology. We cordially thank the Bit-by-Bit project team at the University of Cambridge and the 3DP-RDM network for their funding and
support. We acknowledge the contributions of the technical staff at the 3D Printing Research Group at the University of Nottingham, with
special thanks to Joe White for expertly carrying out the required build experiments.
4. EPSRC Centre for Innovative Manufacturing in Additive Manufacturing
4
Laser Sintering:
a technological baseline
The economics of 3DP: A total cost perspective – Project Report
4
The term Additive Manufacturing is an umbrella
term encompassing a variety of different
technological approaches to the additive, and
normally layer-by-layer, deposition of build
material. The operating principles behind these
approaches range from the selective thermal
fusion of particles held in a powder bed to
the exposure to UV radiation of photoreactive
monomer resins contained in a vat.
This degree of technological variety has led to
considerable difficulties in assessing the viability of
business adoption of AM technology for particular
manufacturing applications. Phrased in the language of
operations management, the conversion mechanisms
with which alternative AM technology variants create
product outputs from raw material inputs differ
considerably. Research has shown that this applies
in particular to build time estimation, process energy
consumption modelling and cost estimation.
It has been noted by technology observers that
the technology variant Laser Sintering (LS) is one
of the most commonly adopted AM technology
variants for the manufacture of end-use products.
Additionally, LS is capable of generating parts
and products with useful mechanical properties.
For these reasons, it was decided to position this
research on LS as a baseline technology, with
additional build experiments conducted on its metallic
counterpart, Selective Laser Melting (SLM).
The LS process operates by feeding polymer powder
into an internal build volume and then spreading it in
a fine layer over the build area, which is located over
a vertically moveable build platform. After preheating
the build material to a suitable temperature below
the melting point, a CO2
laser system is used to
deflect a laser beam to selectively melt the deposited
powder material. Once the exposure process is
complete, the build platform indexes down by one
increment and the cycle repeats. Upon completion of
all layers and sufficient cool down, the build volume
can be removed from the machine for unpacking.
The most commonly employed build material in LS is
a polyamide 12-type powder (nylon). Among many
other uses, components built in this material via LS
have been used in aerospace applications, automotive
and medical products. As with many AM technologies,
LS is also frequently used in prototyping applications.
By selectively building up material within a bed
of unprocessed powder, relatively few geometric
restrictions apply to LS. Additionally, support structures
are not required and geometries can be distributed in
the build volume in three dimensions. These aspects
result in a highly parallel process, allowing the
manufacture of multiple, potentially entirely unrelated,
components within individual build operations.
EOSINT P100
System manufacturer EOS GmbH
Process type Laser Sintering
Energy deposition CO2
laser, 30W
Usable build volume size (X / Y / Z) 260 / 210 / 330 mm
Process atmosphere N2
N2
source N2
generator, internal power supply
Heater type IR and resistance
Melting temperature ~173 ºC
Build material PA2200, Polyamide 12-type thermoplastic
Used layer thickness 100 μm
Support structures Not required
Table 1: Key characteristics of the investigated LS system
5. EPSRC Centre for Innovative Manufacturing in Additive Manufacturing
5
An EOS P100 Laser Sintering system at
the University of Nottingham
The economics of 3DP: A total cost perspective – Project Report
5
6. EPSRC Centre for Innovative Manufacturing in Additive Manufacturing
6
Experiments
To create a body of data for the systematic
analysis of the total costs associated with AM,
it was decided to conduct a series of build
experiments on a state-of-the-art polymeric
EOSINT P100 system, with additional
build experiments performed on a metallic
Renishaw SLM250 platform. Both machines
are located at the 3D Printing Research Group
(3DPRG) at the University of Nottingham.
The main series of build experiments on the polymeric
LS platform comprises ten builds designed to reflect
machine operation at full capacity and four builds
at lower levels of build utilisation. As LS allows the
utilisation of the entire build volume (with a usable
Z-height of 330 mm), running the system at full capacity
results in very extensive builds. To generate a useful
number of repetitions, the project partners decided
to limit the available machine capacity to a horizontal
‘slice’ of the build volume with a Z-height of 30 mm.
A computational build volume packing algorithm
employing a combined front-bottom-left and barymetric
packing heuristic was used to populate the build space
available in each full build experiment. To estimate the
time and resource consumption at full capacity (using
the full available Z-height of 330 mm), the experimental
results were then subject to an extrapolation procedure.
Test geometries manufactured during the
project using Laser Sintering.
The economics of 3DP: A total cost perspective – Project Report
6
7. EPSRC Centre for Innovative Manufacturing in Additive Manufacturing
7
The economics of 3DP: A total cost perspective – Project Report
7
The performed build experiments are based on a
test geometry that was modified for this project.
With overall dimensions of 103 mm × 99 mm × 24
mm, its spider-like shape was chosen because of its
relatively large footprint in the X/Y dimensions to limit
overall packing density to realistic levels. Additionally,
each build experiment contained a number of tensile
specimens for the analysis of material properties.
Figure 1 illustrates the full build configuration in
extrapolated form, using up the available Z-Height
and resulting in a net build volume utilisation
ratio of approximately 10%, which corresponds
to running the machine at full capacity.
A key premise of the approach taken in this project is
to extend the consideration of AM process economics
beyond the core manufacturing step taking place
within the LS machine. To establish this more complete
view, a structured representation of the work flow
encountered in LS was developed by the project
partners. This chain of process steps is shown in
the form of a process map in Figure 2. Effectively
splitting the operation of AM systems into a chain
of discrete operations, the process map was then
translated into spreadsheet form for the collection
of relevant data points from build experiments.
By consistently viewing the AM process as a
sequence of discrete operations, it is possible
to accommodate the cost impact of build failure
and other types of deviation from normal machine
operation. To additionally investigate quality
characteristics of the manufactured parts, each test
geometry was subject to three different procedures:
• a visual inspection for artefacts resulting
from errors in the deposition process;
• measurement of part dimensions to
check for deviation in size;
• destructive testing of tensile specimens
to assess deposited material properties
(acc. to ISO 527-2:1996).
Beyond the experiments on the LS system,
which are in the focus of this report, three
additional build experiments were performed
on the metallic SLM platform, confirming the
applicability of the data collection methodology
and the concept of process maps.
330mm
210mm
260mm
File
preparation
(R1
)
Build set up
on control
system (R2
)
Machine
preparation
(R3
)
Build
release (R4
)
Build
process
Machine
supervision
(R5
)
Build volume
removal,
clean up
(R6
)
Post build
procedures
(S1
)
Unpacking
and powder
handling
(S2
)
Shot
blasting
(U1
)
Part
washing
(U2
)
Commission
parts /
packing
(S3
)
Outright
build
failureYes No
Start
End
Figure 1: Process model at full capacity utilisation
Figure 2: Generic process map surrounding the
core LS process
8. EPSRC Centre for Innovative Manufacturing in Additive Manufacturing
8
Results
This section provides an overview of
experimental results, model specification and
presents of the developed total cost model for
the investigated EOSINT P100 LS system.
Including an additional build to replace rejected test
geometries, a total of 63 test geometries and 56 tensile
specimens were manufactured with a total nominal
volume of 2521 cm³. Over the course of the experiments,
a total of 13.09 kg of virgin PA2200 powder was
introduced to the machine. The material was purchased
from the system vendor at a price of £45.05 per kg. In the
series of build experiments, the EOSINT P100 system
was run at factory settings and configured to a layer
thickness of 100 μm.
The series of planned build experiments comprised 14
builds. Of these 14 experiments, 10 builds reflect system
operation at full capacity, fully using the 30 mm ‘slice’ of
build volume space. The application of an automatic build
volume packing algorithm resulted in the insertion of five
test geometries into the available band of build volume
space. The remaining four build experiments reflect
build configurations with sub-normal levels of build
volume utilisation.
However, due to two outright build failure events directly
affecting the series of experiments, and the requirement
to carry out one additional build to replace rejected test
geometries, 17 build experiments were required in total
on this machine. Table 2 summarises key aspects of this
campaign of build experiments.
The economics of 3DP: A total cost perspective – Project Report
8
Close-up image of the test
geometry showing surface detail.
9. EPSRC Centre for Innovative Manufacturing in Additive Manufacturing
9
The economics of 3DP: A total cost perspective – Project Report
Experiment no. 1 2 3 4 5 6 7 8
Type Planned Planned Planned Planned Planned Planned Planned Repeat build
Capacity utilisation Full Partial Partial Partial Partial Full Full Full
Number of test
geometries included
5 1 2 3 4 5 5 5
Build result Success Success Success Success Success Success Failure Success
Total Z-height
(including blank layers
and tensile bars)
39.1 mm 35.6 mm 35.6 mm 35.6 mm 35.6 mm 39.1 mm 39.1 mm 39.1 mm
Warm up time (incl.
deposition of blank
layers and tensile bars)
234 min 204 min 232 min 206 min 204 min 234 min 207 min 190 min
Net normal build time 149 min 106 min 112 min 121 min 132 min 149 min 15 min 150 min
Cool down time 900 min 600 min 1020 min 840 min 600 min 600 min N/A 840 min
Total build time 1283 min 910 min 1364 min 1167 min 936 min 984 min N/A 1180 min
Table 2: Summary of the build experiments on the EOSINT P100
Experiment no. 9 10 11 12 13 14 15 16 17
Type Planned Planned Planned
Repeat
build
Planned Planned Planned Planned
Additional
build
Capacity utilisation Full Full Full Full Full Full Full Full Partial
Number of test
geometries included
5 5 5 5 5 5 5 5 3
Build result Success Success Failure Success Success Success Success Success Success
Total Z-height
(including blank layers
and tensile bars)
39.1 mm 39.1 mm 39.1 mm 39.1 mm 39.1 mm 39.1 mm 39.1 mm 39.1 mm 35.6 mm
Warm up time (incl.
deposition of blank
layers and tensile bars)
206 min 234 min 164 min 233 min 204 min 199 min 206 min 165 min
Not
measured
Net normal build time 152 min 152 min 70 min 148 min 149 min 151 min 150 min 148 min
Not
measured
Cool down time 960 min 900 min N/A 720 min 960 min 1080 min 960 min 780 min
Not
measured
Total build time 1318 min 1286 min N/A 1101 min 1313 min 1430 min 1316 min 1093 min
Not
measured
9
10. EPSRC Centre for Innovative Manufacturing in Additive Manufacturing
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The economics of 3DP: A total cost perspective – Project Report
10
Effectively, this work adds two additional aspects to the
existing perception of LS as a parallel manufacturing
technology requiring the utilisation of available build
space. Firstly, LS is viewed as embedded in a chain
of discrete process steps incurring additional labour
expenses, as shown in Figure 2. Secondly, some
elements in this chain need to be subject to the
consequences of build failure.
After completion of the build experiments, the next step
within the project was therefore the construction of a unit
cost model capable of reflecting the cost characteristics
of the process itself, as well as the observed risk of
build failure (which is discussed in detail in the following
section) present in the process map of LS. Thus, as an
initial step, the cost model for an LS build, CostBuild,
can
be summarised as follows:
Indirect cost Labour cost
Production overhead rate £4.53 / h Full annual labour costs £32,420 / year
Admin overhead rate £0.31 / h Working days net of holiday 228 days
Total hours worked per year 1653 h
Machine purchase £140,500 Labour cost rate (ĊLabour
) £19.61 / h
Depreciation period 8 years
Annual operating time 5,000 h Direct cost data
Estimated maintenance and consumables £8,516 / year Raw material price £45.05 / kg
Total machine cost rate £5.22 / h Material density, as deposited 0.93 g / cm³
Energy price £0.02 / MJ
Total indirect cost rate (ĊIndirect) £10.06 / h Fixed energy consumption per build 25.23 MJ
Energy consumption rate 1,407.50 J / s
Table 3: Elements of the unit cost model
where P(N) is a function describing the cumulative
probability of build failure after the deposition of N layers,
ĊIndirect
is the indirect cost rate incurred during machine
operation, TBuild
is the core LS build time including warm
up and cool down, CDirect
is an estimate of all indirect costs
incurred for raw material and energy, ĊLabour
is the labour
cost rate, Ri
are the durations attributable per build of all i
process elements in the process map subject to
the risk of build failure, Sj
are the durations attributable
per build of all j process elements not subject to the risk
of this, Uk
are the durations that arise per part of k such
elements and n is the total number of product geometries
included in the build. Following this, the model can be
broken down to the unit cost level, CostUnit
, by attaching a
constant, geometry-determined probability of post-build
part rejection, preject
, which arises on the unit level:
By inserting (1) into (2) and rearranging, the new total
cost model incorporating risk of failure and ancillary
process elements can be expressed as a sum of
elements relating to the core build activity, process chain
elements subject to risk of build failure and elements not
affected by this risk:
As can be seen from this specification, the sum of
all indirect and direct costs associated with the build
activity reflects the cost of the core AM process. This
model is based on an indirect cost rate consisting of
production overheads, administrative overheads and
total machine costs, including depreciation, consumables
and maintenance.
The direct cost estimate captures all expenditures for
material inserted into the system and energy consumed
through the process. The labour cost estimate represents
the costs incurred by the technician executing the build
experiments. Table 3 summarises the elements flowing
into the core process part of the total cost model.
(1)
(2)
(3)
11. 11
As the central result of the model, Figure 3 plots unit
cost estimates against the quantity of test geometries
contained in build processes. The ‘sawtooth’
characteristic of the unit cost curves forms an artefact of
the extrapolation procedure and should thus be ignored.
Importantly, Figure 3 shows unit cost behaviour across the
entire range of capacity utilisation, from highly inefficient
configurations with individual units in the build volume
(grey area) to the fully packed build configurations
shown in Figure 1. The degree of capacity utilisation is
expressed in terms of the build volume utilisation ratio
which is obtained by dividing the total volume of part
geometry included in the build by the volume of the useful
build volume cuboid, as presented in Table 1. In multi-
part builds on LS systems, build volume utilisation ratios
typically range from 5% to 10% in practise. It should be
noted that other criteria are available for the evaluation
of AM capacity utilisation, for example those based on
bounding boxes rather than net geometry.
As shown, in the model including risk of build failure, unit
cost decreases rapidly initially but then begins to rise slightly
as capacity utilisation nears its maximum, as illustrated in
Figure 1. This is due to increases of the expected cost
of build failure overpowering cost benefits of improved
capacity utilisation. As also shown, this behaviour is absent
in the unit cost model excluding risk of build failure.
At full capacity utilisation, corresponding to a build volume
utilisation ratio of approximately 9.11%, the preliminary
results suggest that the total specific cost of operating
the investigated EOSINT P100 system amounts to
£0.89 per cm³ of part geometry deposited. However,
through the presence of risk of build failure at the
measured level, the lowest specific cost is observed
in a configuration with a capacity utilisation rate of
6.62%, at £0.84 per cm³ deposited.
Figure 4 breaks down the total cost of two builds to their
constituent elements at these levels of capacity utilisation
(9.11% and 6.62%). The total cost is separated into the
direct costs of build materials and energy, labour costs
and indirect costs, which include machine depreciation,
maintenance, consumables and overheads. Costs
associated with the risk of build failure, potentially incurred
through the repetition of process elements, are shown as
risk-related costs. Additionally, it is noteworthy that the
highest build rate, 36.47 cm³ per h of machine operation
(excluding tensile specimens), was achieved at full capacity,
corresponding to 9.11% build volume utilisation.
Figure 4: Breakdown of the total costs of the build at utilisation ratios of 9.11% (left) and 6.62% (right)
Figure 3: Unit cost model versus capacity utilisation
Risk-related
costs,
£553.18
Labour costs,
£61.20 Indirect
process costs,
£452.56
Direct costs,
£391.19
Risk-related
costs,
£288.22
Direct costs,
£285.12
Indirect
process costs,
£377.16
Labour costs,
£49.24
Material costs,
£388.23
Energy
costs,
£2.96
Energy costs,
£2.28
Material costs,
£282.84
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
£0
£10
£20
£30
£40
£50
£60
£70
5 10 15 20 25 30 35 40 45 50 55
Quantity of units in the build
Total unit cost estimate (with failure and rejection) Total unit cost estimate (without failure and rejection)
Build volume utilisation ratio
* in the used model, a ratio of 9.11% corresponds to full capacity utilisation, as shown in Figure 1
Totalunitcostpertestgeometry,in£
Buildvolumeutilisationratio*
Inefficientmachineoperationduetounfilledlayers
The economics of 3DP: A total cost perspective – Project Report
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
£0
£10
£20
£30
£40
£50
£60
£70
5 10 15 20 25 30 35 40 45 50 55
Quantity of units in the build
Total unit cost estimate (with failure and rejection) Total unit cost estimate (without failure and rejection)
Build volume utilisation ratio
* in the used model, a ratio of 9.11% corresponds to full capacity utilisation, as shown in Figure 1
Totalunitcostpertestgeometry,in£
Buildvolumeutilisationratio*
Inefficientmachineoperationduetounfilledlayers
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
£0
£10
£20
£30
£40
£50
£60
£70
5 10 15 20 25 30 35 40 45 50 55
Quantity of units in the build
Total unit cost estimate (with failure and rejection) Total unit cost estimate (without failure and rejection)
Build volume utilisation ratio
* in the used model, a ratio of 9.11% corresponds to full capacity utilisation, as shown in Figure 1
Totalunitcostpertestgeometry,in£
Buildvolumeutilisationratio*
Inefficientmachineoperationduetounfilledlayers
12. EPSRC Centre for Innovative Manufacturing in Additive Manufacturing
12
The economics of 3DP: A total cost perspective – Project Report
12
A systematic look
at build failure modes
Underlining the importance of deviation from
normal machine operation for the assessment
of AM process economics, the build experiments
performed for this feasibility study resulted in a
spectrum of build failure events. It is the ambition
of this research to provide a structure for the
classification of such events and to propose a
pathway for their inclusion in studies of AM cost
performance. Three different failure modes
were identified.
Failure mode 1: Outright build failure
The first, and arguably most serious, mode of process
failure encountered is outright build failure. In this case, at
some point during machine operation an unforeseen event
occurs that leads to the premature stoppage of the build
process. In this research, a simplifying assumption is made
that all products contained in the build volume are written
off as a consequence of this termination. In the context
of the build experiments, four outright build failures were
recorded, two during the actual build experiments
(as shown in Table 2), and two in adjacent builds.
Failure mode 2: Post-build part rejection
The second mode of build failure relates to the rejection of
individual parts after completion of the build process. Such
events occur for example if foreign objects are present in
the build volume and disturb the deposition process, or if
there is excessive part deformation due to inadequate cool
down procedures. The manufactured test geometries were
visually and dimensionally assessed for this failure mode.
In total, four test geometries were rejected post-build.
One such rejection was deemed correctable so only three
replacement parts were manufactured.
Failure mode 3: Material failure
The final failure mode investigates the classical
manufacturing defect of non-conformance to material
specification. This aspect is investigated in terms of
variation in material mechanical properties exhibited
by tensile coupons included in each build experiment.
As all assessed test coupons, 56 in total, satisfied the
requirements, this failure model was ignored in the resulting
cost model. This result indicates process stability from a
materials standpoint.
Close-up image of the
result of a build failure event.
13. EPSRC Centre for Innovative Manufacturing in Additive Manufacturing
13
The economics of 3DP: A total cost perspective – Project Report
13
Table 4: Identified modes of build failure
Failure mode 1. Outright build failure 2. Post-build part rejection 3. Material failure
Consequence
Loss of entire build, all contained parts are
written off
Loss of individual parts
Loss of entire build, all
contained parts are written off
Number of occurences 4 events 4 parts None
Model element
Probability of build failure as a function of
cumulative number of depositable layers (N)
Constant probability of part rejection
due to identical test geometries
N/A
Specification
Cumulative distribution function (CDF) P(N)
of normal distribution with mean μ and
standard deviation σ
Fixed probability of rejection preject
N/A
Estimated parameters μ = 4040.75, σ = 3267.95 preject
= 0.07 N/A
Following the identification of build failure modes, it was
possible to associate such events with elements of the total
cost model. As implicit in the process map, shown in Figure 2,
the consequences arising from outright build failure affect only
a subset of elements of the overall AM process chain.
As all process failure occurring in the experimental part of
this work arose during material deposition within the core
AM process, it was assumed that the probability of outright
build failure is determined by the number of consecutively
completed layers, possibly across multiple successive builds.
In order to incorporate this aspect into the cost model, it was
further assumed that the cumulative number of successfully
depositable layers until build failure, N, follows the normal
distribution. Analysis of four outright failure events, which is a
very limited number of observations, allowed the estimation of
the distribution mean at 4041 consecutively deposited layers
and a standard deviation of 3268. On this basis, a cumulative
distribution function P(N | μ,σ) was formulated, allowing the
determination of the probability of build failure as a function of
the number of depositable layers.
A further qualification that must be stated is that this research
treats the probabilistic term of outright build failure P(N | μ,σ)
as unrelated to the product geometry contained within the
build and as independent of the risk of part rejection preject
occurring after the build has been completed. This also implies
that the effect of location within the available build volume on
part quality, and hence risk of rejection, is ignored. All of these
aspects form necessary simplifications at this point in time, but
should be subject to further research into the nature of build
failure modes.
14. 14
Learning effects
14
Despite the prevalent narrative of AM as a fully
automated manufacturing technology requiring
no manual input, the experimental phase of
this research has shown the importance of the
human operator running the process. In reality,
specialised technicians are responsible for the
execution of the steps in the AM work flow, as
encapsulated in the process map (page 7).
For cost minimisation, the technician will attempt
to perform these steps as effectively as possible.
Additionally, he or she takes charge of maintaining the
appropriate raw material levels and composition within
the machine as well as supervising the build process. In
the series of experiments performed on the investigated
LS system, the collected data show that the technician
has manually intervened at least four times, potentially
averting additional build failures.
The economics of 3DP: A total cost perspective – Project Report
Technician Joe White in
discussion with project
investigator Martin Baumers.
15. EPSRC Centre for Innovative Manufacturing in Additive Manufacturing
15
The economics of 3DP: A total cost perspective – Project Report
Figure 5: Makespan of ten identical experiments
1600
1400
1200
1000
800
600
400
200
0
Experiment 1
Experiment 6
Experiment 8
Experiment 9
Experiment 10
Experiment 12
Experiment 13
Experiment 14
Experiment 15
Experiment 16
Makespan,inmin
15
Conclusion
AM technologies represent a significant
opportunity to develop products that previously
could not be manufactured, an opportunity to
make existing products better, and to customise
products to specific needs. The economic case
for adopting AM technologies, however, is as yet
poorly understood.
To address this gap, we have developed a total cost
model for AM processes and measured its key variables
in a series of experiments. As machine technology
advances and aspects such as process stability and
material cost improve further, the economic case for AM
is likely to improve. The value of the parameters in our
model are thus likely to change over time, however the
model itself will remain valid.
Beyond presenting a comprehensive picture of the
process economics of LS, we have furthermore shown
that AM technologies indeed allow for the cost-efficient
creation of product variety. Traditional manufacturing
assumes that one can exploit learning curve effects
(process improvement) by repeating the process. In our
experiments we have shown that – once the technology
is utilised effectively in terms of build volume utilisation
- AM processes do not benefit from repetition. The unit
costs do not decrease in relation to the repetition of the
manufacturing process.
In other words, a central characteristic of traditional
manufacturing is indeed absent in AM processes, which
provides strong empirical evidence for one of the key
claims underpinning the economics of AM technology:
variety can be produced at zero marginal cost.
Aside from the direct involvement with machine operation,
the technician also performs a production planning
function, pooling build requirements to determine build
configurations and specifying support structures where
necessary. In some cases, the technician has additional
discretion in scheduling the machine and deciding the
length of the cool down procedure.
An interesting result from this project relates to the
technician’s productivity. This aspect can be surveyed by
comparing the total process chain duration (makespan)
for each of the ten identical build experiments that were
carried out for this research, as summarised in Table 2.
A chart of these durations in chronological order,
as drawn in Figure 5, does not exhibit a negative
tendency that would be associated with improvements
in productivity, or learning, over time. The insight from
this result is that repetition does not appear to have an
immediate effect on the makespan.
At the current stage, this may serve as an early indication
of the potential absence of learning effects. However,
this aspect may also be explained by the fact that the
technicians operating the investigated systems are usually
qualified expert operators with several years of experience
and extensive training.
16. Martin Baumers
Assistant Professor of Additive
Manufacturing Management
Additive Manufacturing
and 3D Printing Research Group
Faculty of Engineering
The University of Nottingham
NG7 2RD
t: +44 (0)115 951 3877
e: martin.baumers@nottingham.ac.uk
w: www.nottingham.ac.uk/3dprg
Matthias Holweg
Professor of Operations Management
Saïd Business School
University of Oxford
Park End Street
Oxford
OX1 1HP
t: +44 (0)1865 614675
e: matthias.holweg@sbs.ox.ac.uk
w: www.sbs.ox.ac.uk
Jonathan Rowley
Design Director
Digits2Widgets
61 – 63 Rochester Place
London
NW1 9JU
t: +44 (0)20 3697 7969
e: jonathan@digits2widgets.com
w: www.digits2widgets.com