Conference PaperPDF Available

Overview of the Development and Enforcement of Process-Driven Manufacturability Constraints in Product Design

Authors:

Abstract and Figures

Design-for-manufacturing (DFM) concepts have traditionally focused on design simplification; this is highly effective for relatively simple, mass-produced products, but tends to be too restrictive for more complex designs. Effort in recent decades has focused on creating methods for generating and imposing specific , process-derived technical manufacturability constraints for some common problems. This paper presents an overview of the problem and its design implications, a discussion of the nature of the manufacturability constraints, and a survey of the existing approaches and methods for generating/enforcing the minimally-restrictive manufacturability constraints within several design domains. Four major design perspectives were included in the study, including the system design (top-down), the product design (bottom-up), the manufacturing process-dominant approach (specific process required), and the part-redesign approach. Manufacturability constraints within four design levels were explored as well, ranging from macro-scale to sub-micro-scale design. Very little previous work was found in many areas, but it is clear from the existing literature that the problem and a general solution to it are very important to explore further in future DFM and design automation work.
Content may be subject to copyright.
ASME 2019 International Design Engineering Technical Conferences and Computers and
Information in Engineering Conference
IDETC2019
August 18-21, 2019, Anaheim, California, USA
DRAFT: Please see the official conference
proceeding for the final version of this paper
OVERVIEW OF THE DEVELOPMENT AND ENFORCEMENT OF PROCESS-DRIVEN
MANUFACTURABILITY CONSTRAINTS IN PRODUCT DESIGN
Albert E. Patterson1, Yong Hoon Lee2, and James T. Allison1
1Department of Industrial & Enterprise Systems Engineering
2Department of Mechanical Science & Engineering
University of Illinois at Urbana-Champaign, Urbana, IL 61801
Email: {pttrsnv2,ylee196,jtalliso}@illinois.edu
ABSTRACT
Design-for-manufacturing (DFM) concepts have tradition-
ally focused on design simplification; this is highly eective for
relatively simple, mass-produced products, but tends to be too re-
strictive for more complex designs. Eort in recent decades has
focused on creating methods for generating and imposing spe-
cific, process-derived technical manufacturability constraints for
some common problems. This paper presents an overview of the
problem and its design implications, a discussion of the nature
of the manufacturability constraints, and a survey of the existing
approaches and methods for generating/enforcing the minimally-
restrictive manufacturability constraints within several design
domains. Four major design perspectives were included in
the study, including the system design (top-down), the prod-
uct design (bottom-up), the manufacturing process-dominant ap-
proach (specific process required), and the part-redesign ap-
proach. Manufacturability constraints within four design levels
were explored as well, ranging from macro-scale to sub-micro-
scale design. Very little previous work was found in many areas,
but it is clear from the existing literature that the problem and
a general solution to it are very important to explore further in
future DFM and design automation work.
1 INTRODUCTION
Manufacturing is a fundamental step in the design cycle of
every product, one that is often overlooked in the early phases
of design formulation and requirements definition. It is common
for the process selection to be done after the finalization of the
design, speeding up the work but adding risk [13]. If there is
a mismatch between the final design and available manufactur-
ing processes, the design may need to be sent back for additional
iterations, delaying completion and increasing the schedule and
cost risk [47]. If the final product is relatively simple or a tried-
and-true basic design that was previously developed, the man-
ufacturing is usually very straight-forward, and the risk is low.
However, for more complex designs (such as those created using
algorithms, e.g., topology optimization or generative design), it
is possible for final designs to be completely unmanufacturable
with any of the available processes [810]. In the worst case, the
design process may need to be reversed several steps or started
over to incorporate the new lessons learned by the design team
during the manufacturing attempt (Figure 1). This is not depen-
dent on any particular lifecycle design method [1,5,8] and could
be applicable for a linear model (Figure 1) as well as agile [11],
evolutionary [12,13], and iterative models [14], as well as others.
Requirements
and concepts Preliminary
design Final
design Manufacturing
& assembly
Manufacturable?
Yes
No
New constraints and requirements
from manufacturing domain
Level 1Level 2Level 3
FIGURE 1:Manufacturability check and potential loop-back
when design is mismatched with manufacturing process
To address this in part, design-for-manufacturing (DFM)
principles have been developed in recent decades [8,1517]. As
a technical approach, DFM has commonly referred to a set of de-
sign heuristics in which the design is simplified as much as possi-
ble to reduce the risk of mismatch with a manufacturing process
and risk to the budget and schedule of the product development.
There traditionally have been a wide variety of these rules, which
can be summarized as five basic guidelines [7,8,18,19]:
Make the design as simple as possible
Use common, cheap, and easy-to-process materials
Use as many standard components as possible in a system
1 Copyright c
2019 by ASME
Make the tolerances as liberal as possible, except when they
can be eectively allocated without large trade-os
Consult manufacturing personnel for design decisions
The most important characteristic of these rules is that they
are process- and material-independent and are typically very
generic [8,15,20]. This version of DFM is extremely eective
in a mass-production environment with simple or established de-
signs, but tends to be overly-restrictive for specialized or com-
plex designs and results in designs favoring simplicity over opti-
mality [8,21,22]. In a mass-customization paradigm, such as the
one emerging in recent years [2325], it is vital for designers to
fully utilize the design space and optimize the design as much as
possible [2629]. This is especially important when producing
small-batch, customized, high-value parts such as those needed
for aircraft and medical devices. Therefore, a DFM technique
which would restrict the design space only enough to guarantee
manufacturability is needed. To ensure the minimum restriction
on the design space, it is necessary to replace the general heuris-
tics with well-defined constraints driven directly by the charac-
teristics of the processes or methods selected.
FIGURE 2:Example domain applicability for formative, subtrac-
tive, and additive manufacturing processes relative to manufac-
turing cost and available design complexity
The domains of applicability for the three major species of
manufacturing processes (subtractive, additive, and formative)
are dierent and potentially complementary [30,31]. Figure 2
shows an example practical arrangement of the three process ar-
eas related to allowed design complexity and manufacturing cost,
with a few representative processes in each family identified. Ar-
eas of complementarity (domain overlaps) could be utilized in
the form of hybrid processes [3133]. Note that this figure does
not consider material choice, production volume, or manufac-
turing tolerances, which would help determine the most practical
process to use and justify a higher manufacturing cost if required.
With this in mind, any manufacturing process can be said
to be subject to a set of natural manufacturing constraints which
aect its use domain and which must be considered in the de-
sign process to achieve a design that is both ideal and manufac-
turable. In addition, it is necessary to consider manufacturability
constraints, which are on the design itself and are in response
to the manufacturing constraints. For example, a machined alu-
minum part design would be constrained by the tool size, speed,
and cutting rate of the mill [30] (manufacturing constraints) and
a minimum feature size to ensure that the part could dissipate
the heat and force of machining without warping [34,35] (man-
ufacturability constraint). The design “ownership” in each do-
main is dierent, with production engineers best understanding
the manufacturing constraints. This requires excellent commu-
nication between the production team and the designers, a task
that is not always performed eectively [3,8,10,15,16]. More
general mapping approaches have been suggested for translating
manufacturing constraints directly into manufacturability con-
straints [5,9,31], but this is an immature area and needs much
additional research.
It should be noted that, while both the manufacturing and
manufacturability constraints need to be considered during de-
sign, designers do not have the same level of control for each.
The manufacturing constraints are natural and inherent in the
process itself, and therefore typically not controllable beyond
developing new processes or selecting one with acceptable lim-
itations [8,30,36]. The manufacturability constraints, on the
other hand, are driven by the manufacturing constraints, but also
allow the designer to modify or control their magnitude. In
mathematical terms, the manufacturing constraints are generally
equality constraints (fixed value) and the manufacturability con-
straints are inequality (boundary) constraints. With this in mind,
it should be noted also that the manufacturability constraints are
more likely to be continuous functions (e.g., the range of wall
thicknesses on a cast part), while the manufacturing constraints
are more likely to take the form of combinatorial or discrete
functions (e.g., a list of cutting tool sizes for a milling process).
Showing that the manufacturability constraints are not violated
for a particular design (via heuristic or monotonicity analysis, or
other methods) is a good proof that the design is likely manufac-
turable without needing to use DFM to restrict the design space.
The purpose of the work presented here was to survey the ex-
isting DFM literature to find the state-of-the-art for the definition
of the manufacturing constraints, and the generation and enforce-
ment of the manufacturability constraints. An extensive search
was performed in important design and manufacturing journals,
conference proceedings, and searches in Scopus, Web of Sci-
ence, and Google Scholar. Note that this is a design-practice
focused survey and not a formal, systematic review paper. This
work is structured into several sections, plus discussion and con-
clusions:
Section 2: An overview of manufacturing processes and
their natural constraints
Section 3: Overview of DFM impact on several approaches,
focusing on system-level (top-down) design, product-level
(bottoms-up) design, the case where a specific manufactur-
ing process is required, and part-redesign
Section 4: DFM level analysis at several design levels rang-
ing from macro-level to sub-micro-level considerations
2 Copyright c
2019 by ASME
FIGURE 3:Design-related process characteristics for SM, AM, and FM, shown with examples of common processes and common
manufacturing constraints for processes within each domain
2 PROCESSES AND MfG CONSTRAINTS
Most standard (non-hybrid) manufacturing processes fall
into one of three major families, namely subtractive,additive,
and formative (Figure 2) [30]. There are numerous finishing, as-
sembly, and validation processes as well, but this paper focused
on the material processing aspects of manufacturing, and so these
were not examined. Basic characteristics (Figure 3) are:
Subtractive: SM processes form geometry by cutting ma-
terial away from a block or billet which is larger than the
desired final shape [30,3638]. SM requires little custom
tooling besides fixtures and jigs [39], but the design geome-
try is restricted to that which can be reached by standardized
cutting tools; the features must also be large enough resist
the machining force and allow sucient heat transfer since
the tools produce friction heat [34,35,40]. For appropriate
designs, SM is a very cheap, repeatable, and ecient man-
ufacturing approach; it is very wasteful, however, due to the
large amount of material cut oin processing [41].
Additive: AM builds up the desired geometry in layers, al-
lowing great design freedom and highly complex parts [42].
The raw material can take many forms, as long as it can be
layered and fused onto a surface in some fashion [43,44].
Ideally, the process generates very little waste but most de-
signs require a fixture and support material [45]. The pro-
cess requires almost no custom tooling, and part complexity
does not drive the cost, but can be extremely slow and ex-
pensive in some cases [42,46,47].
Formative: FM has the largest diversity of processes, as the
only requirement to be a formative process is that material
needs to be shaped or formed into the final part somehow.
The raw material may be a cold billet, molten metal, pow-
der, resin, and many other things. As with AM, FM pro-
duces little to no waste; however, it requires a large amount
of custom tooling to produce parts, and the geometry is re-
stricted to the shape and quality of the molds and other tool-
ing [30,36,4851].
In general, SM processes tend to have the most restriction
on the types of part features that can be created due to the essen-
tial requirement that cutting tools be able to reach all of the part
from some force point (commonly a rotating spindle) [5254].
AM, by definition, does not have tooling-related complexity re-
strictions, but there are some restrictions due to support material
removal [55,56], natural material anisotropy [57,58], and pro-
cess mechanics [42,43]; however, the possible design complex-
ity is very high for most of the AM processes [42,43,59]. Con-
versely, FM is almost entirely dependent on the tooling used and
is limited to the tooling complexity. In the most common case,
the tooling (molds, forging tools, and similar) must be made us-
ing some SM process, which limits its complexity to that which
can be cut or machined [30,4851]. However, some FM pro-
cesses can use free-form or shell molds (for example, investment
casting) which dramatically enhances the possible part complex-
ity [36,6062].
Of the three major domains, AM has the widest range of
available materials when all of the major families are considered;
the various AM processes can use almost any material which
can somehow be applied in a layer and fused with a previous
layer [42,43,63]. AM materials are most commonly in the form
of filament, resin, or powder, but may be as diverse as water
(ice prototyping [64]) or rolled metal sheets (ultrasonic consoli-
dation [65]). In general, SM materials are limited to those which
can easily be cut with a tool and can tolerate the associated heat
load, usually ductile metals and hard polymers [30,36]. On the
other hand, FM materials are limited to those that can be stably
melted or cold-formed to conform with some tooling [30,48,50].
This is less restrictive than SM, being able to process various
bulk and molten materials, resins, and metal powders, but less
free than AM because of the dependence on tooling.
Due to the need only for standard clamps and fixtures [30,
36,39] for single parts, SM tends to be able to produce one-o
parts relatively cheaply compared to AM and FM. However, it
can be more expensive to mass-produce parts using SM because
3 Copyright c
2019 by ASME
of the need for the special fixtures, jigs, and higher quality cutting
tools than needed for one-oparts [30,36]. The cost for one-o
AM parts is high due to the expensive nature of the processing
equipment and materials, as well as the generally slow process-
ing speed; unlike SM, AM can be relatively cheaper to perform
mass production for some (not all) complex designs since the
manufacturing time and cost is mostly dependent on total part
volume and not complexity [43,66]. The supply chain for AM,
within the available set of processes and materials, is also often
more ecient and less prone to blockages [42,43]. Finally, FM is
very expensive for single parts and very cheap for mass produc-
tion, making it ideal for many products. The reason for the high
up-front cost is the tooling initial cost, but this goes down dra-
matically as the tool is used more [30,36]; the raw materials for
FM are generally much cheaper than those for SM and AM (since
they will be formed or melted during processing, high quality
finish and precision in the materials is usually not necessary), the
supply chain is very ecient, and one good set of tooling may
last for hundreds of thousands of parts [30,50,51].
3 MANUFACTURABILITY CONSTRAINTS: DESIGN
APPROACH PERSPECTIVES
In the preceding section, the three major classes of manu-
facturing processes and their common constraints were explored.
Careful consideration of these constraints and their potential im-
pact on design allows the development of customized DFM ap-
proaches for specific problems; this, in turn, allows the designer
to restrict the available design space just enough to ensure man-
ufacturability. This section examines the various specific DFM
methodologies developed within four essential design perspec-
tives in which DFM has been applied eectively. These are (1)
the system design (top-down) perspective, (2) the product design
(bottom-up) perspective, (3) the case where a specific manufac-
turing process is required, and (4) the part-redesign perspective.
3.1 System Design (Top-Down) Perspective
In the system design (top-down) design perspective, the goal
of design is to consider the construction of a system or subsystem
and is less concerned with the optimal design of individual parts;
while optimization of each part is important, it is more impor-
tant in top-down design for each part of the system to be optimal
relative to overall system utility [2,6,67]. From the DFM per-
spective, the focus will be to make the manufacturing process se-
lection such that the parts are manufacturable in an ecient way,
and such that the materials and tolerances are compatible. The
business case for considering a DFM technique is easy to make,
as it prevents re-design and resulting delays, as well as ensuring
the the possible design space is as large as possible [5,6870].
The most obvious application of within this domain is the
improvement of any general lifecycle design technique, such as
those proposed by NASA [1], INCOSE [67], Pahl et al. [6], and
Blanchard & Fabrycky [2]. Within such a design engine, the
more general DFM approaches usually are applicable, allowing
either the use of DFM and the optimal selection of manufactur-
ing processes after design is completed [8,16]. While the general
engine does not necessarily need customized DFM methods (es-
pecially if the design is very simple), when the lifecycle design
approach is applied to a particular domain, the use of minimal-
DFM can be very valuable.
This value can be especially apparent in previous work done
on aircraft design. Generally, aircraft parts have very tight toler-
ances, need to be very lightweight, and need to be highly consis-
tent, which dramatically limits the available manufacturing pro-
cesses for these parts [68,69,71]. The set-based concurrent de-
sign technique proposed by Vallhagen et al. [71] uses a type of
custom DFM technique to eliminate clearly infeasible manufac-
turing processes early in the design and allows the accommoda-
tion of process constraints at several points in the lifecycle. A
similar approach focused on ensuring that all of the parts have
compatible tolerances and that the various system interfaces are
producible was developed by Barbosa & Carvalho [69].
Electronics and mechatronics design is an important appli-
cation of DFM at the system level. The 2003 study by Bajaj et
al. [72] explored this in detail, developing a rule-based system
for finding and imposing the relevant constraints (of several op-
tions available from the system to the designer) to accomplish
a good quality design. Several studies by W.H. Wood [73,74],
Shetty et al. [75], and Berselli et al. [76] discussed some of the
major issues when designing mechatronic systems and presented
a framework for considering formal (mathematical) and heuristic
manufacturability constraints related to both the mechanical and
electronics sides of the design.
3.2 General Product Design (Bottom-Up) Perspective
The design perspective with the most direct benefit from the
use of minimally-restrictive DFM is design of individual parts.
When the design focus is bottom-up (i.e. the system is built from
several products individually developed) and each part must be
optimized individually, the largest possible expansion of the de-
sign space is needed. It is assumed in this case that a specific
manufacturing process has not been required by the customer
and the designer is free to select the one that provides the least
restrictive manufacturing profile and design space.
In most of the DFM studies found on part design, a specific
manufacturing process was defined in the problem statement and
so it was not true bottom-up design (where it is assumed that per-
formance is the primary goal and several production processes
may be possible) [77,78]; these cases will be discussed in the
proceeding section. The work found in this area was primarily in
the domain of decision analysis, where the manufacturability re-
quirements or guidelines are discovered and fed back into the de-
sign process as it developed. Works by Barnawal et al. [20] and
Budinoet al. [79] analyzed this in detail, showing that eec-
tive communication of the constraints and manufacturing expec-
tations was the key to ensuring product manufacturability; this
was shown to be true for both heuristic, experienced-based con-
straints and formal mathematical manufacturability constraints.
A large and detailed case study on the mathematical defini-
tion and enforcement of manufacturability constraints was com-
pleted by Iyengar & Bar-Cohen [80] in which a side-inlet-side-
exit (SISE) parallel plate heat exchanger was developed con-
straints for eight dierent processes (extrusion, two types of die
casting, bonding, folding, forging, skiving, and machining); it
was found that feasible solutions for the design existed under
4 Copyright c
2019 by ASME
each process constraint set, but the constraints were clearly active
and provided very dierent optimal solutions based on the pro-
cess selected. Similarly, several studies by Vatanabe et al. [9],
Guest & Zhu [81], Mantovani et al. [82], Zuo et al. [83], and
Reddy et al. [84] have examined the impact of manufacturability
constraints on topology optimization (TO) solutions for several
dierent manufacturing processes simultaneously, with results
similar to the heat exchanger problem described above. Since
TO is an algorithm-based design process, the manufacturability
constraints are usually enforced inside of the algorithm. For ex-
ample, the study by Vatanabe et al. applied manufacturability
constraints for six dierent processes (casting, milling, turning,
extrusion, rolling, and forging), producing a variety of dier-
ent topologies under these constraints. The constraints were en-
forced in the form of topology constraints, such as minimum fea-
ture sizes, symmetry, and avoiding undercuts, within the mathe-
matical formulation of the problem.
3.3 Manufacturing Process Perspective
This section continues the discussion from the previous sec-
tion on product design, with a manufacturing process specified.
3.3.1 SM Processes In general, machining requires
a careful tool-path planning to ensure that all of the geome-
try can be cut with the tools [85]; this is true for both man-
ual and computer-controlled machines. For example, Monge
et al. [86] proposed a three-step process for designing turbine
blades by generating an optimal shape based on a combined set
of constraints from a computation fluid dynamics (CFD) model
and an optimal toolpath generator; the solution found produced
both an improved design and one that was manufacturable using
a machining process. More general solutions were developed
by Kang et al. [87], Deja & Siemiatkowski [88], and Gupta &
Nau [89], which are based on feature clustering and checking the
optimality of a series of cutting path plans which open the de-
sign space as much as possible. In addition to path planning for
conventionally-designed parts, machining constraints have been
developed for use in TO-generated designs as well. Projection-
based TO can be very eectively constrained for machining, as it
is based on continuous geometric constraints and interfaces well
with a toolpath, as shown by Guest & Zhu [81]. Specific machin-
ing and milling-related constraints have also been developed for
a few cases within the level-set TO approach [90,91], as well as
heavyside projection, gradient, and hybrid methods [83,92].
3.3.2 AM Processes Most of the work done so far in
establishing and enforcing manufacturability constraints for AM
processes has been for the development of design rules, some for
general AM and some for specific processes. The focus of exten-
sive studies by Jee & Witherell [93], Adam & Zimmer [94,95],
Bin Maidin et al. [96], and Kranz et al. [97] was on the develop-
ment of standardized feature databases in which the AM manu-
facturing constraints could be applied to standard common part
features to ensure manufacturability. The designer could then se-
lect the features from the database that are best for the design
at hand while ensuring manufacturability. In a more focused ef-
fort, Tang et al. [98] presented a method for developing a unit
structure-performance database to allow discrete optimization of
light-weight housings via selective laser melting; this technique
for arranging small standard features to optimize a design is use-
ful and complementary with the feature catalogs developed in the
previously-mentioned works.
Using the results from an extensive literature survey, Pradel
et al. [99] proposed a framework for mapping of AM process
knowledge for product design. They describe the need for more
“practical” application of AM in design and suggest several
methods for achieving this for general processes. Some work
has been performed to establish AM constraints in TO [100,101],
similar to those discussed in the previous section, but this is still
an immature area and needs additional attention. Design rules
and constraints for specific AM process have been proposed by
several scholars. Thompson et al. [56] point out that many of the
process limitations in AM come from the modeling and software
used to drive the processes, but that this is an area where progress
is being made.
In addition to more general AM constraints (minimal fea-
ture size [102], overhangs [55], surface roughness, avoidance
of stress concentrations [58], material anisotropy [57], support
material removal [103], among other things), some processes
have more specific constraints which must be considered. While
many of these are not well characterized, a lot of work has
been done for some of the very common processes. For ex-
ample, Utley et al. [104], Thomas [105], and Kranz & Her-
zog [97] proposed a series of manufacturability constraints for
the selective laser melting (SLM) process directly driven by the
process characteristics. These SLM constraints are things such
as delamination, laser heat deformation, potential oxidation be-
tween the material layers, and scan pattern constraints specific
to laser scanning processes such as SLM. Similar work has been
done for selective laser sintering (SLS) [106,107] and electron
beam melting (EBM) [108110], which have similar manufac-
turing constraints, with EBM generally being less restrictive than
SLS/SLM due to the use of a heated chamber.
Other specific processes for which process-specific design
rules have been developed include fused deposition modeling
(FDM) [111113], stereolithography (SLA) [114,115], material
jetting [116], and binder jetting [117]. The general design lim-
itations cited from FDM are in the area of minimal feature size
(more strict than standard AM constraints), support material de-
sign, and surface accuracy and finish. FDM, material jetting, and
SLA have similar manufacturability constraints, with the excep-
tion that SLA and material jetting have less strict minimal fea-
ture size restrictions. Binder jetting, which uses powder as the
raw material, has constraints similar to those of the powder bed
processes (SLM, SLS, EBM) mentioned above except for those
related to heat warping.
3.3.3 FM Processes An area of significant interest in
minimally-restrictive DFM has been in the use of casting pro-
cesses to fabricate complex geometry generated by topology op-
timization (TO) algorithms. In the major studies reviewed, this
is done by mapping the major casting/FM constraints [118] into
the design within level-set [119,120], gradient [121], and pro-
jection [9,81,122] methods to generate a topology that is cast-
able. Casting constraints are well-developed for TO, since they
5 Copyright c
2019 by ASME
are much less strict than those for machining processes, and can
be defined simply in terms of thickness and a requirement that
the geometry be continuous; these constraints ensure that the liq-
uefied material can flow into the mold and reach all features, can
dissipate the heat, and that a parting line can be established.
Some work has also been completed on the TO-based design
of parts to be fabricated using an extrusion or drawing process.
The manufacturability constraints for extrusion are much more
simple than those for casting. When using a projection-based TO
method, as done by Vatanabe et al. [9], the constraints are sim-
ply applied to a “slice” of the part; the domain is automatically
continuous in an extrusion process, so the manufacturability con-
straints consist mainly of avoiding features that are too delicate
to survive being pushed or drawn through a die. Li et al. [123]
and Sutradhar et al. [10] showed that this can also be done using
a type of internal projection within a level-set TO method.
In addition to DFM-based TO solutions in casting and ex-
trusion, some work has gone into finding conventional (non-TO)
design rules for closed-tooling processes, particularly injection
molding, die casting, and powder metallurgy. Injection molding
is typically limited to plastics (e.g., ABS or silicone), die casting
to ductile metals (e.g., zinc or aluminum), and powder metallurgy
to metal powder (sometimes mixed with a binder); manufactura-
bility analysis within the appropriate tooling is focused primarily
on being able to quickly and eciently fill the mold with mate-
rial and eject it safely. The manufacturability constraints then are
in the form of feature restrictions (they must fit into and be easily
removable from the tool), usually with a two-part tool, and the
location of the tool parting line [124127]. Powder metallurgy
is the least restrictive [30,128], as it can sometimes use a four-
part tool instead of the standard two-part used in injection mold-
ing and die casting. Extensive work has gone into simulation of
these processes in order to better understand how the material
can flow into the tool and solidify in the way intended by the de-
signer [129133]; these simulations can be used to guide designs
but generally are used just to check manufacturability and plan
the process after the completion of the design.
3.4 Part-Redesign Perspective
From the perspective of green manufacturing, the primary
value of the use of manufacturability constraints (besides the pre-
vention of inecient design and manufacturing) is in the area
of re-design. Parts subjected to re-design are generally tech-
nically manufacturable but the designer has identified areas of
improvement in the manufacturing or assembly. The redesign
of parts specifically to make them more ecient or less ex-
pensive to manufacture was the subject of several studies for
milled [134,135], turned [136], and stamped [137] parts, as well
as the production of part families [138]. While not technically
DFM, this redesign approach is interesting as it shows a need for
tightening manufacturability constraints once problems or inef-
ficiencies are discovered after completion of the design. These
problems could have been avoided by using proper DFM during
original design, eliminating the need for corrective action later.
4 MANUFACTURABILITY CONSTRAINTS: DESIGN
LEVEL PERSPECTIVES
The design of features and part details can be completed at
dierent design levels, each of which requires dierent kinds of
manufacturability constraints. The main dierence, from a de-
sign perspective, of each of the levels is the feature size created
within each domain. The macro-level is defined as containing
features at least a millimeter in size, while meso-level features
may range from a few hundred micrometers to one millimeter,
the micro-level may range from one to a few hundred microme-
ters, and sub-micro-scale is less than one micrometer in size.
4.1 Macro-Level Design
One of the major tasks when designing at this level is the
generation and refinement of macro-level structures and aggre-
gates such as lattices, overhangs, mounting bosses, and similar
features. Design at this level is generally straight-forward, and
is usually done using design rules and feature catalogs which
provide manufacturable features. Definition of these rules for
most traditional manufacturing processes (such as machining and
injection molding) is based on simple DFM principles, as dis-
cussed in depth in Sections 3.2 and 3.3. Figure 4a shows an
injection-molding caliper case, which is an example of a stan-
dard product with macro-scale features.
Fabrication of macro-scale features for AM processes is
more complex due to the layered nature of the resulting mate-
rial and the presence of natural voids, stress concentrations, and
residual stresses [58,141]. While it is important to use feature
catalogs and feature families, the manufacturability constraints
will be more strict than they would for more simple processes.
Research has been performed specifically for AM processes; for
example, the studies by Adam & Zimmer [94,95] and Bin Maidin
et al. [96] developed a list of macro-level standard design fea-
tures and their transitions. The rules presented are developed for
several specific AM processes and incorporate process knowl-
edge directly from these processes into the design of edges, wall
thicknesses, gap heights, and other design features. Some AM
processes (such as SLM) require the ability of the material to
transfer heat rapidly during processing and small features need
to be adjusted for this, including controlling the porosity [142].
Maximum length scale constraints for structural and fluid topol-
ogy optimization is another important application; it can limit the
size flow channels and structural members as needed, as shown
by Guest [143] and Lazerov & Wang [144].
4.2 Meso-Level Design
The primary applications found for meso-level design were
in the design of meso-scale features which act like a controllably-
anisotropic material. Since, in most cases, the material for parts
made using SM and FD process is approximately isotropic, this
design level has been applied mainly to additively-fabricated
parts. The use of AM to design and build meso-level materi-
als structures was explore by several studies; Chu et al. [145],
Yu at al. [146], and Garcia et al. [147] developed a theoretical
framework for single- and multi-material problems, while Siva-
puram et al. [148], Gopsill et al. [149], and Gardan et al. [150]
explored the practical implications and requirements for using
6 Copyright c
2019 by ASME
FIGURE 4:Examples of design features at various levels. (a) macro-scale injection-molded caliper case, (b) meso-scale 3-D printed
thin-walled structures, (c) micro-scale electrodes [139], and (d) sub-micro-scale LED pits [140]
AM to build meso-scale tailored materials. Examples of some
AM-generated mesostructured materials are shown in Figure 4b.
4.3 Micro-Level Design
Manufacturing constraints derived for micro-scale features
and parts (Figure 4c) could be more restrictive than larger-scale
designs due to the small size of the dimensions. Most conven-
tional manufacturing processes, including casting, forging, ma-
chining, and additive manufacturing, do not have the capacity
to fabricate extremely small geometry; therefore, it is vital that a
production process be selected and considered in the design stage
to make sure the final product is manufacturable.
The small number of manufacturing processes that can re-
liably fabricate at the the micro-scale have been reviewed, so
the capabilities if these processes are known and can be used to
generate manufacturability constraints. For example, Ashman &
Kandlikar [151] examined several types of manufacturing pro-
cesses for fabricating heat exchangers with hydraulic diameter
of less than 200 micrometers. Etsion [152] presented a compre-
hensive review on micro-level laser surface texturing (LST) in
connection with hydrodynamic lubrication and wear reduction as
well as surface texturing in general. Romig et al. [153] discussed
issues of micro-electro-mechanical systems (MEMS) design and
fabrication in various aspects, including materials, manufactura-
bility, performance, and reliability. AM-based fabrication has
been discussed by Frazier et al. [154] and Dede et al. [155]; while
AM oers great potential for this, there are clear problems with
the processes that need to be addressed before they can be eec-
tively used for micro-scale fabrication. These include material
defects, anisotropic properties (which aect the fabrication more
for smaller geometries), inconsistent cooling, residual stresses,
complex material behavior, and other related concerns.
In addition to feature size restrictions, the topologies and
shapes also should have specific constraints in order to be fab-
ricated at this scale. Considering a micro-milling process with
a ball end mill, Lee et al. [156] applied a spline-interpolated
smooth free surface with a maximum slope angle as a manufac-
turability constraint in the surface texture design-for-lubrication
problem. Even though the target design size is larger than micro-
level, features in the design may still be smaller than those which
can be fabricated at this level by certain processes. Specifically,
keeping the feature size larger than the manufacturing resolution
should not be overlooked in topology and shape optimization.
Sigmund [157,158] showed examples of manufacturing failure
due to feature size, and introduced robust topology optimization
frameworks that can filter out infeasibly small features.
4.4 Sub-Micro-Level Design
While the micro-scale design and fabrication of parts and
features is very challenging, accomplishing it at the sub-micro-
scale is even more restrictive and dicult. However, this is an
extremely important design scale and many important applica-
tions demand features at this size. Examples include friction
and wear reduction [159,160], nano-electro-mechanical systems
(NEMS) [161], and superhydrophobic surfaces [162]. These de-
sign features are typically part of larger-scale parts and assem-
blies, and may require additional manufacturability constraints
compared to those established elsewhere in the design. Sub-
micro-level surface treatment using micro- and nano-texturing
and surface modification strategies are similar to those discussed
in previous sections, except that the tolerances are much tighter
and the manufacturability constraints are very restrictive. Sub-
micro-scale surface texturing and treatment methods for corro-
sion and wear resistance often involve combinations of ther-
mal, electrochemical, and mechanical processes, which alter
surface electrochemical and molecular properties, mechanical
shapes and patterns, or sometimes material itself by applying
coatings [163]. Often, sub-micro-level features and parts are
manufactured using the same or similar techniques that are ap-
plied to fabricated nano-scale structures; these fabrication tech-
niques can be typically classified into two categories: top-down
and bottom-up approaches.
Top-down fabrication approaches mostly utilize nanolithog-
raphy, deposition, and etching processes. Conventionally, this
approach is used in the semiconductor industries, but the usage
is expanding to more intricated applications, including NEMS,
sensors and actuators, optoelectronics, as it is capable of fabri-
cating structures down to nanometer resolution [161]. Due to the
layered nature of fabrication processes, the top-down approach
is mainly limited to 2D or 2.5D structures in manufacturabil-
ity. Structures can be fabricated by repeated material deposition
and removal processes, supporting very accurate manufactur-
7 Copyright c
2019 by ASME
ing, but presenting manufacturability problems when the length
scale is less than a few nanometers [164,165]. The bottom-up
approach places material at the desired locations, similar to 3-
D printing processes. Currently, a direct-write nano-deposition
(specifically, two-photon polymerization, 2PP) method is avail-
able to fabricate structures smaller than the micrometer level eas-
ily, and at its limits down to a length scale of approximately
50 nm [166,167]. This approach has similar characteristics and
constraints to what is commonly seen in 3D printing; however,
even with the wide freedom in shape and topologies that AM
enables, postprocessing of structures fabricated using nanoscale
AM via 2PP is still challenging. The main challenge is the re-
moval of support structure and any extra raw material, as this is
very dicult or impossible when dealing with extremely small
parts [168].
5 CONCLUSIONS AND FUTURE WORK
The purpose of this work was the explore the generation
and imposition of process-driven manufacturability constraints
for product design problems. First, a description of the prob-
lem was presented, showing that many designs require the use of
manufacturability constraints as a strategy to take advantage of
the largest possible design space. Next, the various major manu-
facturing processes and their common manufacturing constraints
were discussed in depth. After discussion of the manufacturing
constraints, the design literature was explored from several dif-
ferent perspectives and levels for existing approaches in applying
process-driven manufacturability constraints to design problems.
Four dierent design perspectives were examined; first from the
perspective of system-based design, then product-level design for
both the general case and the case where a manufacturing process
is specified, and finally from the perspective of part re-design to
address manufacturability problems. Four dierent design lev-
els or scales were analyzed, ranging from standard macro-scale
design to sub-micro-scale problems.
This work focused on design under single, non-hybrid man-
ufacturing processes that are standardized and with which most
designers should be familiar; joining processes (such as welding)
and secondary manufacturing (i.e., the production of manufactur-
ing tools) were not considered, as they were beyond the scope of
this work and are deserving of their own in-depth reviews. The
design and fabrication of material microstructure were also not
addressed in the present study, as numerous other works have ex-
plored it in great depth. A new field of part redesign for emerging
technologies (instead of redesign to address manufacturability
problems) has been developing over the past several years, but
is not yet mature and was not examined in this work.
Some important observations and conclusions were made af-
ter reviewing the collected literature on the topic:
A formal, generic method for mapping manufacturability
constraints from any manufacturing process to any design
problem does not yet exist, but much progress has been
made for some specific problems.
Parts conventionally-designed (i.e., not designed using an
algorithm) under several common FM and SM processes
do not appear to have formally-defined methods for ensur-
ing manufacturability of the parts beyond visual observation
and rules-of-thumb. Especially noted were investment cast-
ing, blanking/coining/stamping, turning/facing processes,
rolling, and forging processes.
The design of conventional sand and shell casting parts seem
to be completed using mainly heuristic-based design and tra-
ditional DFM principles (i.e., "make it simple").
In top-down (system-level) design, the manufacturability
constraints need to consider global as well as local manu-
facturability problems.
In bottom-up (product) design, the same product can have
vastly dierent final designs from the same starting point
when active manufacturability constraints for dierent pro-
cesses are considered.
The established manufacturability constraints for SM pro-
cesses tend to be related to surface topology, while AM con-
straints generally relate to part cross-section and material be-
havior, and FM constraints seem to be driven primarily by
material behavior when interacting with and being removed
from the tooling.
Part re-design solutions presented in the literature to address
manufacturability problems show that a simple and eec-
tive way to address manufacturing problems is to tighten the
manufacturability constraints for the design.
If it can be shown that all the manufacturability constraints
are inactive, it is very likely that the design is manufac-
turable without the constraints.
The smaller the design scale, the more restrictive the manu-
facturability constraints become and the fewer process types
are capable of fabrication.
Future work should focus on addressing the areas where
minimally-restrictive manufacturability constraints are not in
regular use, as they can help to open up the design space and al-
low the further optimization of the design. There is a great need
for a standardized (whether formally-standardized or in common
use) method for mapping the manufacturability constraints di-
rectly to design constraints. If this can be developed and au-
tomated, it could dramatically speed up the design process and
increase its reliability for new areas of design exploration.
Acknowledgments
No external funding was used to perform the work described
in this study. Opinions and conclusions presented in this work are
solely those of the authors.
References
[1] NASA, 2017. NASA Systems Engineering Handbook: NASA/Sp-2016-6105 Rev2 -
Full Color Version. 12th Media Services.
[2] Blanchard, B. S., and Fabrycky, W. J., 2005. Systems Engineering and Analysis (4th
Edition). Prentice Hall.
[3] Lutters, E., van Houten, F. J., Bernard, A., Mermoz, E., and Schutte, C. S., 2014.
“Tools and techniques for product design”. CIRP Annals, 63(2), pp. 607–630.
[4] Lee, E. A., and Xiong, Y., 2001. “System-level types for component-based design”.
In Embedded Software. Springer Berlin Heidelberg, pp. 237–253.
[5] Ferrer, I., Rios, J., and Ciurana, J., 2009. “An approach to integrate manufacturing
process information in part design phases”. Journal of Materials Processing Technol-
ogy, 209(4), Feb., pp. 2085–2091.
[6] Pahl, G., Beitz, W., Feldhusen, J., and Grote, K. H., 2007. Engineering Design: A
Systematic Approach (3rd Edition). Springer.
[7] Boothroyd, G., 1994. “Product design for manufacture and assembly”. Computer-
Aided Design, 26(7), July, pp. 505–520.
8 Copyright c
2019 by ASME
[8] Bralla, J. G., 1998. Design for Manufacturability Handbook (2nd Edition). McGraw-
Hill Education.
[9] Vatanabe, S. L., Lippi, T. N., de Lima, C. R., Paulino, G. H., and Silva, E. C., 2016.
“Topology optimization with manufacturing constraints: A unified projection-based
approach”. Advances in Engineering Software, 100, Oct., pp. 97–112.
[10] Sutradhar, A., Park, J., Haghighi, P., Kresslein, J., Detwiler, D., and Shah, J. J., 2017.
“Incorporating manufacturing constraints in topology optimization methods: A sur-
vey”. In Volume 1: 37th Computers and Information in Engineering Conference,
ASME.
[11] Gunasekaran, A., 1999. “Agile manufacturing: A framework for research and devel-
opment”. International Journal of Production Economics, 62(1-2), pp. 87–105.
[12] Eastman, C., and Jeng, T. S., 1999. A database supporting evolutionary product
model development for design”. Automation in Construction, 8(3), pp. 305–323.
[13] Tay, F. E. H., and Gu, J., 2003. “A methodology for evolutionary product design”.
Engineering with Computers, 19(2-3), pp. 160–173.
[14] Sunnerjo, S., Cederfeldt, M., Elgh, F., and Rask, I., 2006. A transparent design
system for iterative product development”. Journal of Computing and Information
Science in Engineering, 6(3), p. 300.
[15] Herrmann, J. W., Cooper, J., Gupta, S. K., Hayes, C. C., Ishii, K., Kazmer, D., Sand-
born, P. A., and Wood, W. H., 2004. “New directions in design for manufacturing”.
In Volume 3d: 8th Design for Manufacturing Conference, ASME.
[16] Pullan, T. T., Bhasi, M., and Madhu, G., 2010. “Application of concurrent engi-
neering in manufacturing industry”. International Journal of Computer Integrated
Manufacturing, 23(5), May, pp. 425–440.
[17] Howard, L., and Lewis, H., 2003. “The development of a database system to optimise
manufacturing processes during design”. Journal of Materials Processing Technol-
ogy, 134(3), pp. 374–382.
[18] Li, Z., Izquierdo, L. E., Kokkolaras, M., Hu, S. J., and Papalambros, P. Y., 2008.
“Multiobjective optimization for integrated tolerance allocation and fixture layout de-
sign in multistation assembly”. Journal of Manufacturing Science and Engineering,
130(4), p. 044501.
[19] Li, Z., Kokkolaras, M., Papalambros, P., and Hu, S. J., 2008. “Product and pro-
cess tolerance allocation in multistation compliant assembly using analytical target
cascading”. Journal of Mechanical Design, 130(9), p. 091701.
[20] Barnawal, P., Dorneich, M. C., Frank, M. C., and Peters, F., 2017. “Evaluation of
design feedback modality in design for manufacturability”. Journal of Mechanical
Design, 139(9), July, p. 094503.
[21] Hu, S. J., 2013. “Evolving paradigms of manufacturing: From mass production to
mass customization and personalization”. Procedia CIRP, 7, pp. 3–8.
[22] Duguay, C. R., Landry, S., and Pasin, F., 1997. “From mass production to flexi-
ble/agile production”. International Journal of Operations &Production Manage-
ment, 17(12), pp. 1183–1195.
[23] Chu, W.-S., Kim, M.-S., Jang, K.-H., Song, J.-H., Rodrigue, H., Chun, D.-M., Cho,
Y. T., Ko, S. H., Cho, K.-J., Cha, S. W., Min, S., Jeong, S. H., Jeong, H., Lee, C.-M.,
Chu, C. N., and Ahn, S.-H., 2016. “From design for manufacturing (DFM) to man-
ufacturing for design (MFD) via hybrid manufacturing and smart factory: A review
and perspective of paradigm shift”. International Journal of Precision Engineering
and Manufacturing-Green Technology, 3(2), pp. 209–222.
[24] Jiao, J., and Tseng, M. M., 2004. “Customizability analysis in design for mass cus-
tomization”. Computer-Aided Design, 36(8), pp. 745–757.
[25] Tseng, M., Jiao, R., and Wang, C., 2010. “Design for mass personalization”. CIRP
Annals, 59(1), pp. 175–178.
[26] Hazelrigg, G. A., 1998. “A framework for decision-based engineering design”. Jour-
nal of Mechanical Design, 120(4), p. 653.
[27] Gries, M., 2004. “Methods for evaluating and covering the design space during early
design development”. Integration, the VLSI Journal, 38(2), pp. 131–183.
[28] Kim, I. Y., and Kwak, B. M., 2002. “Design space optimization using a numeri-
cal design continuation method”. International Journal for Numerical Methods in
Engineering, 53(8), pp. 1979–2002.
[29] Gelsey, A., Schwabacher, M., and Smith, D., 1998. “Using modeling knowledge to
guide design space search”. Artificial Intelligence, 101(1-2), pp. 35–62.
[30] Black, J. T., and Kohser, R. A., 2011. DeGarmo’s Materials and Processes in Manu-
facturing (11th Edition). Wiley.
[31] Patterson, A. E., and Allison, J. T., 2018. “Manufacturability constraint formula-
tion for design under hybrid additive-subtractive manufacturing”. In ASME IDETC:
Volume 4: 23rd Design for Manufacturing and the Life Cycle Conference, ASME.
[32] Karunakaran, K., Suryakumar, S., Pushpa, V., and Akula, S., 2010. “Low cost in-
tegration of additive and subtractive processes for hybrid layered manufacturing”.
Robotics and Computer-Integrated Manufacturing, 26(5), pp. 490–499.
[33] Zhu, Z., Dhokia, V., Nassehi, A., and Newman, S., 2013. A review of hybrid manu-
facturing processes – state of the art and future perspectives”. International Journal
of Computer Integrated Manufacturing, 26(7), pp. 596–615.
[34] Archenti, A., Osterlind, T., and Nicolescu, C. M., 2011. “Evaluation and repre-
sentation of machine tool deformations”. Journal of Machine Engineering, 11(4),
pp. 105–117.
[35] Mayr, J., Jedrzejewski, J., Uhlmann, E., Donmez, M. A., Knapp, W., Härtig, F.,
Wendt, K., Moriwaki, T., Shore, P., Schmitt, R., Brecher, C., Würz, T., and Wegener,
K., 2012. “Thermal issues in machine tools”. CIRP Annals, 61(2), pp. 771–791.
[36] Kalpakjian, S., and Schmid, S. R., 2001. Manufacturing Engineering and Technology
(4th ed.). Prentice-Hall: Upper Saddle River, NJ, USA.
[37] El-Hofy, H., 2013. Fundamentals of Machining Processes. CRC Press.
[38] Davim, J. P., 2008. Machining: Fundamentals and Recent Advances. Springer Lon-
don.
[39] Li, K., Liu, R., Bai, G., and Zhang, P., 2006. “Development of an intelligent jig and
fixture design system”. In 2006 7th International Conference on Computer-Aided
Industrial Design and Conceptual Design, IEEE.
[40] Li, K.-M., and Liang, S. Y., 2006. “Modeling of cutting temperature in near dry
machining”. Journal of Manufacturing Science and Engineering, 128(2), p. 416.
[41] Pusavec, F., Krajnik, P., and Kopac, J., 2010. “Transitioning to sustainable production
– part i: application on machining technologies”. Journal of Cleaner Production,
18(2), pp. 174–184.
[42] Gibson, I., Rosen, D., and Stucker, B., 2016. Additive Manufacturing Technologies:
3D Printing, Rapid Prototyping, and Direct Digital Manufacturing. Springer.
[43] Guo, N., and Leu, M. C., 2013. “Additive manufacturing: technology, applications
and research needs”. Frontiers of Mechanical Engineering, 8(3), May, pp. 215–243.
[44] Mueller, B., 2012. “Additive manufacturing technologies – rapid prototyping to direct
digital manufacturing”. Assembly Automation, 32(2).
[45] Huang, S. H., Liu, P., Mokasdar, A., and Hou, L., 2012. “Additive manufacturing
and its societal impact: a literature review”. The International Journal of Advanced
Manufacturing Technology, 67(5-8), Oct., pp. 1191–1203.
[46] Baumers, M., Dickens, P., Tuck, C., and Hague, R., 2016. “The cost of additive man-
ufacturing: machine productivity, economies of scale and technology-push”. Techno-
logical Forecasting and Social Change, 102, pp. 193–201.
[47] Atzeni, E., and Salmi, A., 2012. “Economics of additive manufacturing for end-
usable metal parts”. The International Journal of Advanced Manufacturing Technol-
ogy, 62(9-12), pp. 1147–1155.
[48] Beddoes, J., and Bibby, M., 1999. Principles of Metal Manufacturing Processes.
Butterworth-Heinemann: Oxford, UK.
[49] Campbell, J., 2015. Complete Casting Handbook: Metal Casting Processes, Metal-
lurgy, Techniques, and Design (2nd ed.). Butterworth-Heinemann: Oxford, UK.
[50] Strong, A. B., 2005. Plastics: Materials and Processing (3rd ed.). Pearson: London.
[51] Datta, B., 2014. Powder Metallurgy: An Advanced Technique of Processing Engi-
neering Materials. Prentice-Hall: Upper Saddle River, NJ, USA.
[52] Nishida, I., Sato, R., and Shirase, K., 2016. “Process planning system of 5-axis ma-
chining center considering constraint condition”. In 2016 International Symposium
on Flexible Automation (ISFA), IEEE.
[53] Xu, K., Wang, J., Chu, C.-H., and Tang, K., 2017. “Cutting force and machine
kinematics constrained cutter location planning for five-axis flank milling of ruled
surfaces”. Journal of Computational Design and Engineering, 4(3), pp. 203–217.
[54] Zhang, Y., Zhang, D., and Wu, B., 2015. “An approach for machining allowance
optimization of complex parts with integrated structure”. Journal of Computational
Design and Engineering, 2(4), pp. 248–252.
[55] Jiang, J., Xu, X., and Stringer, J., 2018. “Support structures for additive manufactur-
ing: A review”. Journal of Manufacturing and Materials Processing, 2(4), p. 64.
[56] Thompson, M. K., Moroni, G., Vaneker, T., Fadel, G., Campbell, R. I., Gibson, I.,
Bernard, A., Schulz, J., Graf, P., Ahuja, B., and Martina, F., 2016. “Design for ad-
ditive manufacturing: Trends, opportunities, considerations, and constraints”. CIRP
Annals, 65(2), pp. 737–760.
[57] Ahn, S.-H., Montero, M., Odell, D., Roundy, S., and Wright, P. K., 2002.
“Anisotropic material properties of fused deposition modeling ABS”. Rapid Pro-
totyping Journal, 8(4), pp. 248–257.
[58] Patterson, A. E., Messimer, S. L., and Farrington, P. A., 2017. “Overhanging features
and the SLM/DMLS residual stresses problem: Review and future research need”.
Technologies, 5(2), p. 15.
[59] Rebong, R. E., Stewart, K. T., Utreja, A., and Ghoneima, A. A., 2018. “Accuracy of
three-dimensional dental resin models created by fused deposition modeling, stere-
olithography, and polyjet prototype technologies: A comparative study”. The Angle
Orthodontist, 88(3), pp. 363–369.
[60] Jones, S., and Yuan, C., 2003. “Advances in shell moulding for investment casting”.
Journal of Materials Processing Technology, 135(2-3), pp. 258–265.
[61] Sabau, A. S., and Viswanathan, S., 2003. “Material properties for predicting wax
pattern dimensions in investment casting”. Materials Science and Engineering: A,
362(1-2), pp. 125–134.
[62] Jiang, W., Fan, Z., Liu, D., Liao, D., Dong, X., and Zong, X., 2013. “Correlation of
microstructure with mechanical properties and fracture behavior of a356-t6 aluminum
alloy fabricated by expendable pattern shell casting with vacuum and low-pressure,
gravity casting and lost foam casting”. Materials Science and Engineering: A, 560,
pp. 396–403.
[63] ASTM, 2012. ASTM F2792-12a: Standard Terminology for Additive Manufacturing
Technologies. ASTM International.
[64] Feng, C., Yan, S., Zhang, R., and Yan, Y., 2007. “Heat transfer analysis of rapid ice
prototyping process by finite element method”. Materials &Design, 28(3), pp. 921–
927.
[65] Friel, R., and Harris, R., 2013. “Ultrasonic additive manufacturing – a hybrid pro-
duction process for novel functional products”. Procedia CIRP, 6, pp. 35–40.
[66] Thomas, D., 2015. “Costs, benefits, and adoption of additive manufacturing: a supply
chain perspective”. The International Journal of Advanced Manufacturing Technol-
ogy, 85(5-8), pp. 1857–1876.
[67] INCOSE, 2015. INCOSE Systems Engineering Handbook: A Guide for System Life
Cycle Processes and Activities. Wiley.
[68] Knight, W., 1991. “Design for manufacture analysis: Early estimates of tool costs for
sintered parts”. CIRP Annals, 40(1), pp. 131–134.
[69] Barbosa, G., and Carvalho, J., 2013. “Design for manufacturing and assembly
methodology applied to aircrafts design and manufacturing”. IFAC Proceedings Vol-
umes, 46(7), pp. 116–121.
[70] Ferrer, I., Rios, J., Ciurana, J., and Garcia-Romeu, M., 2010. “Methodology for cap-
turing and formalizing DFM knowledge”. Robotics and Computer-Integrated Manu-
facturing, 26(5), pp. 420–429.
9 Copyright c
2019 by ASME
[71] Vallhagen, J., Isaksson, O., Söderberg, R., and Wärmefjord, K., 2013. A framework
for producibility and design for manufacturing requirements in a system engineering
context”. Procedia CIRP, 11, pp. 145–150.
[72] Bajaj, M., Peak, R., Wilson, M., Kim, I., Thurman, T., Jothishankar, M.,
Benda, M., Ferreira, P., and Stori, J., 2003. “Towards next-generation design-
for-manufacturability (DFM) frameworks for electronics product realization”. In
IEEE/CPMT/SEMI 28th International Electronics Manufacturing Technology Sym-
posium, 2003. IEMT 2003., IEEE.
[73] Dong, H., and Wood, W. H., 2003. “Issues in integration of design and manufacturing
for mechatronics”. In ASME IDETC: Volume 3a: 8th Design for Manufacturing
Conference, ASME.
[74] Wood, W. H., and Agogino, A. M., 2005. “Decision-based conceptual design: Mod-
eling and navigating heterogeneous design spaces”. Journal of Mechanical Design,
127(1), p. 2.
[75] Shetty, D., Poudel, N., and Ososanya, E., 2015. “Design of robust mechatronics
embedded systems by integration of virtual simulation and mechatronics platform”.
In Volume 2B: Advanced Manufacturing, ASME.
[76] Berselli, G., Borghesan, G., Brandi, M., Melchiorri, C., Natale, C., Palli, G., Pirozzi,
S., and Vassura, G., 2009. “Integrated mechatronic design for a new generation of
robotic hands”. IFAC Proceedings Volumes, 42(16), pp. 8–13.
[77] Fathianathan, M., and Panchal, J. H., 2008. “Modelling an ongoing design process
utilizing top-down and bottom-up design strategies”. Proceedings of the Institu-
tion of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223(5),
pp. 547–560.
[78] Thomas, M., and McGarry, F., 1994. “Top-down vs. bottom-up process improve-
ment”. IEEE Software, 11(4), pp. 12–13.
[79] Budino, H. D., McMains, S., and Rinaldi, A., 2018. “An interactive manufactura-
bility analysis and tolerance allocation tool for additive manufacturing”. In Volume
2A: 44th Design Automation Conference, ASME.
[80] Iyengar, M., and Bar-Cohen, A., 2001. “Design for manufacturability of SISE par-
allel plate forced convection heat sinks”. IEEE Transactions on Components and
Packaging Technologies, 24(2), pp. 150–158.
[81] Guest, J. K., and Zhu, M., 2012. “Casting and milling restrictions in topology op-
timization via projection-based algorithms”. In Volume 3: 38th Design Automation
Conference, Parts A and B, ASME.
[82] Mantovani, S., Presti, I. L., Cavazzoni, L., and Baldini, A., 2017. “Influence of
manufacturing constraints on the topology optimization of an automotive dashboard”.
Procedia Manufacturing, 11, pp. 1700–1708.
[83] Zuo, K.-T., Chen, L.-P., Zhang, Y.-Q., and Yang, J., 2005. “Manufacturing- and
machining-based topology optimization”. The International Journal of Advanced
Manufacturing Technology, 27(5-6), pp. 531–536.
[84] K., S. N. R., Maranan, V., Simpson, T. W., Palmer, T., and Dickman, C. J., 2016. “Ap-
plication of topology optimization and design for additive manufacturing guidelines
on an automotive component”. In Volume 2A: 42nd Design Automation Conference,
ASME.
[85] Lu, Y. A., Ding, Y., Wang, C., and Zhu, L., 2018. “Tool path generation for five-
axis machining of blisks with barrel cutters”. International Journal of Production
Research, pp. 1–15.
[86] Monge, J., Vessaz, C., Avellan, F., and Tournier, C., 2013. “Integration of machining
constraints in design optimization of a guide vane cascade”. In 10th International
Conference on Computer Aided Design, Jun 2013, Bergamo, Italy.
[87] Kang, M., Han, J., and Moon, J., 2003. “An approach for interlinking design and
process planning”. Journal of Materials Processing Technology, 139(1-3), pp. 589–
595.
[88] Deja, M., and Siemiatkowski, M. S., 2012. “Feature-based generation of machining
process plans for optimised parts manufacture”. Journal of Intelligent Manufacturing,
24(4), pp. 831–846.
[89] Gupta, S. K., and Nau, D. S., 1995. “Systematic approach to analysing the manufac-
turability of machined parts”. Computer-Aided Design, 27(5), pp. 323–342.
[90] Liu, J., and Ma, Y. S., 2015. “3d level-set topology optimization: a machining feature-
based approach”. Structural and Multidisciplinary Optimization, 52(3), pp. 563–582.
[91] Liu, J., Yu, H., and Ma, Y., 2016. “Minimum void length scale control in level set
topology optimization subject to machining radii”. Computer-Aided Design, 81, dec,
pp. 70–80.
[92] Liu, J., and Ma, Y., 2016. A survey of manufacturing oriented topology optimization
methods”. Advances in Engineering Software, 100, Oct., pp. 161–175.
[93] Jee, H., and Witherell, P., 2017. “A method for modularity in design rules for additive
manufacturing”. Rapid Prototyping Journal, 23(6), pp. 1107–1118.
[94] Adam, G. A., and Zimmer, D., 2014. “Design for additive manufacturing—element
transitions and aggregated structures”. CIRP Journal of Manufacturing Science and
Technology, 7(1), pp. 20–28.
[95] Adam, G. A. O., and Zimmer, D., 2015. “On design for additive manufacturing:
evaluating geometrical limitations”. Rapid Prototyping Journal, 21(6), pp. 662–670.
[96] Maidin, S. B., Campbell, I., and Pei, E., 2012. “Development of a design feature
database to support design for additive manufacturing”. Assembly Automation, 32(3),
pp. 235–244.
[97] Kranz, J., Herzog, D., and Emmelmann, C., 2015. “Design guidelines for laser addi-
tive manufacturing of lightweight structures in TiAl6v4”. Journal of Laser Applica-
tions, 27(S1), p. S14001.
[98] Tang, L., Zhang, Q., Liang, K., Zhao, X., and Zhang, Z., 2018. “Discrete optimization
of internal part structure via SLM unit structure-performance database”. Metals, 8(1),
p. 45.
[99] Pradel, P., Zhu, Z., Bibb, R., and Moultrie, J., 2018. “A framework for mapping de-
sign for additive manufacturing knowledge for industrial and product design”. Jour-
nal of Engineering Design, 29(6), pp. 291–326.
[100] Mhapsekar, K., McConaha, M., and Anand, S., 2018. “Additive manufacturing con-
straints in topology optimization for improved manufacturability”. Journal of Manu-
facturing Science and Engineering, 140(5), p. 051017.
[101] Rezayat, H., Bell, J. R., Plotkowski, A. J., and Babu, S. S., 2018. “Multi-solution
nature of topology optimization and its application in design for additive manufactur-
ing”. Rapid Prototyping Journal. DOI 10.1108/rpj-01-2018-0009.
[102] Weiss, B., Diegel, O., Storti, D., and Ganter, M., 2018. “A process for estimating
minimum feature size in selective laser sintering”. Rapid Prototyping Journal, 24(2),
pp. 436–440.
[103] Mirzendehdel, A. M., and Suresh, K., 2016. “Support structure constrained topology
optimization for additive manufacturing”. Computer-Aided Design, 81, pp. 1–13.
[104] Utley, E., 2018. “Designing for 3D printing: direct metal laser sintering”. In Laser
3D Manufacturing V, H. Helvajian, A. Piqué, and B. Gu, eds., SPIE.
[105] Thomas, D., 2009. “The development of design rules for selective laser melting”. PhD
thesis, CardiMetropolitan University, Cardi, UK. Available at https://repository.
cardimet.ac.uk/handle/10369/913.
[106] Seepersad, C., 2012. Design rules for selective laser sintering. Tech. rep., Univer-
sity of Texas-Austin, Austin, Texas, USA. Available at https://www.me.utexas.edu/
~ppmdlab/files/designers.guide.sls.pdf.
[107] Allison, J., Sharpe, C., and Seepersad, C. C., 2019. “Powder bed fusion metrology for
additive manufacturing design guidance”. Additive Manufacturing, 25, pp. 239–251.
[108] Cheng, B., and Chou, Y. K., 2017. “Overhang support structure design for elec-
tron beam additive manufacturing”. In Volume 2: Additive Manufacturing Materials,
ASME.
[109] Ameen, W., Al-Ahmari, A., Mohammed, M., and Mian, S., 2018. “Manufacturability
of overhanging holes using electron beam melting”. Metals, 8(6), p. 397.
[110] Sing, S. L., An, J., Yeong, W. Y., and Wiria, F. E., 2015. “Laser and electron-beam
powder-bed additive manufacturing of metallic implants: A review on processes, ma-
terials and designs”. Journal of Orthopaedic Research, 34(3), pp. 369–385.
[111] Urbanic, R. J., and Hedrick, R., 2016. “Fused deposition modeling design rules for
building large, complex components”. Computer-Aided Design and Applications,
13(3), pp. 348–368.
[112] Messimer, S., Pereira, T., Patterson, A., Lubna, M., and Drozda, F., 2019. “Full-
density fused deposition modeling dimensional error as a function of raster angle and
build orientation: Large dataset for eleven materials”. Journal of Manufacturing and
Materials Processing, 3(1), p. 6.
[113] Kouhi, E., Masood, S., and Morsi, Y., 2008. “Design and fabrication of reconstructive
mandibular models using fused deposition modeling”. Assembly Automation, 28(3),
pp. 246–254.
[114] Pham, D. T., and Ji, C., 2000. “Design for stereolithography”. Proceedings of the
Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Sci-
ence, 214(5), pp. 635–640.
[115] Davoudinejad, A., Diaz-Perez, L. C., Quagliotti, D., Pedersen, D. B., Albajez-García,
J. A., Yagüe-Fabra, J. A., and Tosello, G., 2018. “Additive manufacturing with vat
polymerization method for precision polymer micro components production”. Pro-
cedia CIRP, 75, pp. 98–102.
[116] Meisel, N., and Williams, C., 2015. “An investigation of key design for additive
manufacturing constraints in multimaterial three-dimensional printing”. Journal of
Mechanical Design, 137(11), p. 111406.
[117] Gardan, J., 2017. “Method for characterization and enhancement of 3D printing by
binder jetting applied to the textures quality”. Assembly Automation, 37(2), pp. 162–
169.
[118] Harzheim, L., and Graf, G., 2005. “A review of optimization of cast parts using topol-
ogy optimization. II-Topology optimization with manufacturing constraints”. Struc-
tural and Multidisciplinary Optimization, 31(5), pp. 388–399.
[119] Allaire, G., Jouve, F., and Michailidis, G., 2013. “Casting constraints in structural
optimization via a level-set method”. In 10th World Congress on Structural and Mul-
tidisciplinary Optimization, Orlando, FL, USA.
[120] Wang, Y., and Kang, Z., 2017. “Structural shape and topology optimization of cast
parts using level set method”. International Journal for Numerical Methods in Engi-
neering, 111(13), Jan., pp. 1252–1273.
[121] Gersborg, A. R., and Andreasen, C. S., 2011. “An explicit parameterization for cast-
ing constraints in gradient driven topology optimization”. Structural and Multidisci-
plinary Optimization, 44(6), Mar., pp. 875–881.
[122] Guest, J. K., Prévost, J. H., and Belytschko, T., 2004. Achieving minimum length
scale in topology optimization using nodal design variables and projection functions”.
International Journal for Numerical Methods in Engineering, 61(2), Aug., pp. 238–
254.
[123] Li, H., Li, P., Gao, L., Zhang, L., and Wu, T., 2015. “A level set method for topo-
logical shape optimization of 3d structures with extrusion constraints”. Computer
Methods in Applied Mechanics and Engineering, 283, Jan., pp. 615–635.
[124] Bidkar, R. A., and McAdams, D. A., 2009. “Methods for automated manufacturabil-
ity analysis of injection-molded and die-cast parts”. Research in Engineering Design,
21(1), pp. 1–24.
[125] Fagade, A., and Kazmer, D., 1998. “Economic design of injection molded parts using
dfm guidelines - a review of two methods for tooling cost estimation”. In Proceedings
of ANTEC98, Society of Plastic Engineers, Atlanta, GA, USA, SPE, pp. 869–873.
[126] Fu, M., Fuh, J., and Nee, A., 1999. “Generation of optimal parting line direction
based on undercut features in injection molded parts”. IIE Transactions, 31(10),
pp. 947–955.
[127] Singh, R., and Madan, J., 2013. “Systematic approach for automated determination
of parting line for die-cast parts”. Robotics and Computer-Integrated Manufacturing,
29(5), pp. 346–366.
10 Copyright c
2019 by ASME
[128] Smith, L. N., 1999. A knowledge based system for optimum and concurrent de-
sign, and manufacture by powder metallurgy technology”. International Journal of
Production Research, 37(1), pp. 125–137.
[129] Spina, R., Spekowius, M., and Hopmann, C., 2016. “Multiphysics simulation of
thermoplastic polymer crystallization”. Materials &Design, 95, pp. 455–469.
[130] Choi, S.-J., and Kim, S. K., 2011. “Multi-scale filling simulation of micro-injection
molding process”. Journal of Mechanical Science and Technology, 25(1), pp. 117–
124.
[131] Niedziela, D., Tröltzsch, J., Latz, A., and Kroll, L., 2011. “On the numerical sim-
ulation of injection molding processes with integrated textile fiber reinforcements”.
Journal of Thermoplastic Composite Materials, 26(1), pp. 74–90.
[132] Tercan, H., Guajardo, A., Heinisch, J., Thiele, T., Hopmann, C., and Meisen, T., 2018.
“Transfer-learning: Bridging the gap between real and simulation data for machine
learning in injection molding”. Procedia CIRP, 72, pp. 185–190.
[133] Shi, J., Cheng, Z., Barriere, T., Liu, B., and Gelin, J. C., 2017. “Multiphysic cou-
pling and full cycle simulation of microwave sintering applied to a ceramic compact
obtained by ceramic injection moulding”. Powder Metallurgy, 60(5), pp. 404–414.
[134] Adalier, M., and Tsatsoulis, C., 1992. “Redesigning for manufacturability using
REINRED”. Applied Artificial Intelligence, 6(3), pp. 285–302.
[135] Hatcher, G., Ijomah, W., and Windmill, J., 2011. “Design for remanufacture: a liter-
ature review and future research needs”. Journal of Cleaner Production, 19(17-18),
pp. 2004–2014.
[136] Yannoulakis, N. J., Joshi, S. B., and Wysk, R. A., 1994. “Quantitative measures of
manufacturability for rotational parts”. Journal of Engineering for Industry, 116(2),
p. 189.
[137] Hayes, C., 1996. “Plan-based manufacturability analysis and generation of shape-
changing redesign suggestions”. Journal of Intelligent Manufacturing, 7(2).
[138] Lee, B., and Saitou, K., 2002. “Design of part family robust-to-production plan vari-
ations based on quantitative manufacturability evaluation”. Research in Engineering
Design, 13(4), pp. 199–212.
[139] Tran, V.-T., Wei, Y., Liau, W., Yang, H., and Du, H., 2017. “Preparing of inter-
digitated microelectrode arrays for AC electrokinetic devices using inkjet printing of
silver nanoparticles ink”. Micromachines, 8(4), p. 106.
[140] Chen, S.-W., Li, H., Chang, C.-J., and Lu, T.-C., 2017. “Eects of nanoscale v-shaped
pits on GaN-based light emitting diodes”. Materials, 10(2), p. 113.
[141] Eiliat, H., and Urbanic, J., 2018. “Visualizing, analyzing, and managing voids in the
material extrusion process”. The International Journal of Advanced Manufacturing
Technology, 96(9-12), pp. 4095–4109.
[142] Brandt, M., Sun, S. J., Leary, M., Feih, S., Elambasseril, J., and Liu, Q. C., 2013.
“High-value SLM aerospace components: From design to manufacture”. Advanced
Materials Research, 633, pp. 135–147.
[143] Guest, J. K., 2008. “Imposing maximum length scale in topology optimization”.
Structural and Multidisciplinary Optimization, 37(5), pp. 463–473.
[144] Lazarov, B. S., and Wang, F., 2017. “Maximum length scale in density based topology
optimization”. Computer Methods in Applied Mechanics and Engineering, 318,
pp. 826–844.
[145] Chu, C., Graf, G., and Rosen, D. W., 2008. “Design for additive manufacturing of
cellular structures”. Computer-Aided Design and Applications, 5(5), pp. 686–696.
[146] Yu, H. Z., Cross, S. R., and Schuh, C. A., 2017. “Mesostructure optimization in multi-
material additive manufacturing: a theoretical perspective”. Journal of Materials
Science, 52(8), pp. 4288–4298.
[147] Garcia, D., Jones, M. E., Zhu, Y., and Yu, H. Z., 2017. “Mesoscale design of het-
erogeneous material systems in multi-material additive manufacturing”. Journal of
Materials Research, 33(01), pp. 58–67.
[148] Sivapuram, R., Dunning, P. D., and Kim, H. A., 2016. “Simultaneous material and
structural optimization by multiscale topology optimization”. Structural and Multi-
disciplinary Optimization, 54(5), pp. 1267–1281.
[149] Gopsill, J. A., Shindler, J., and Hicks, B. J., 2017. “Using finite element analysis to
influence the infill design of fused deposition modelled parts”. Progress in Additive
Manufacturing, 3(3), pp. 145–163.
[150] Gardan, J., Makke, A., and Recho, N., 2017. “Improving the fracture toughness of 3d
printed thermoplastic polymers by fused deposition modeling”. International Journal
of Fracture, 210(1-2), dec, pp. 1–15.
[151] Ashman, S., and Kandlikar, S. G., 2006. “A review of manufacturing processes for
microchannel heat exchanger fabrication”. In Proceedings of the ASME 4th Interna-
tional Conference on Nanochannels, Microchannels, and Minichannels, Parts A and
B, pp. 855–860.
[152] Etsion, I., 2005. “State of the art in laser surface texturing”. Journal of Tribology,
127(1), pp. 248–253.
[153] Romig Jr., A. D., Dugger, M. T., and McWhorter, P. J., 2003. “Materials issues in
microelectromechanical devices: science, engineering, manufacturability and relia-
bility”. Acta Materialia, 51(19), pp. 5837–5866.
[154] Frazier, W. E., 2014. “Metal additive manufacturing: a review”. Journal of Materials
Engineering and Performance, 23(6), pp. 1917–1928.
[155] Dede, E. M., Joshi, S. N., and Zhou, F., 2015. “Topology optimization, additive
layer manufacturing, and experimental testing of an air-cooled heat sink”. Journal of
Mechanical Design, 137(11), Oct., p. 111403.
[156] Lee, Y. H., Schuh, J. K., Ewoldt, R. H., and Allison, J. T., 2017. “Enhancing full-film
lubrication performance via arbitrary surface texture design”. Journal of Mechanical
Design, 139(5), p. 053401.
[157] Sigmund, O., 2007. “Morphology-based black and white filters for topology opti-
mization”. Structural and Multidisciplinary Optimization, 33(4-5), pp. 401–424.
[158] Sigmund, O., 2009. “Manufacturing tolerant topology optimization”. Acta Mechan-
ica Sinica, 25(2), pp. 227–239.
[159] Baharin, A. F. S., Ghazali, M. J., and Wahab, J. A., 2016. “Laser surface texturing and
its contribution to friction and wear reduction: a brief review”. Industrial Lubrication
and Tribology, 68(1), pp. 57–66.
[160] Gittens, R. A., McLachlan, T., Olivares-Navarrete, R., Cai, Y., Berner, S., Tannen-
baum, R., Schwartz, Z., Sandhage, K. H., and Boyan, B. D., 2011. “The eects of
combined micron-/submicron-scale surface roughness and nanoscale features on cell
proliferation and dierentiation”. Biomaterials, 32(13), pp. 3395–3403.
[161] Dew, S. K., and Stepanova, M., 2012. “Directions in nanofabrication”. In Nanofab-
rication: Techniques and Principles, M. Stepanova and S. K. Dew, eds. Springer-
Verlag, Wien, ch. 1, pp. 3–8.
[162] Onda, T., Shibuichi, S., Satoh, N., and Tsujii, K., 1996. “Super-water-repellent fractal
surfaces”. Langmuir, 12(9), pp. 2125–2127.
[163] Zhao, W., Wang, L., and Xue, Q., 2010. “Influence of micro/nano-textures and chem-
ical modification on the nanotribological property of au surface”. Colloids and Sur-
faces A: Physicochemical and Engineering Aspects, 366(1-3), pp. 191–196.
[164] Chiang, C., and Kawa, J., 2007. Design for Manufacturability and Yield for Nano-
Scale CMOS. Integrated Circuits and Systems. Springer, Dordrecht.
[165] Kelly, M. J., 2011. “Intrinsic top-down unmanufacturability”. Nanotechnology, 22,
p. 245303.
[166] Maruo, S., Nakamura, O., and Kawata, S., 1997. “Three-dimensional microfab-
rication with two-photon-absorbed photopolymerization”. Optics Letters, 22(2),
pp. 132–134.
[167] de Miguel, G., Vicidomini, G., Harke, B., and Diaspro, A., 2016. “Linewidth and
writing resolution”. In Three-Dimensional Microfabrication Using Two-photon Poly-
merization. Elsevier, pp. 190–220.
[168] Waheed, S., Cabot, J. M., Macdonald, N. P., Lewis, T., Guijt, R. M., Paull, B., and
Breadmore, M. C., 2016. “3d printed microfluidic devices: enablers and barriers”.
Lab on a Chip, 16(11), pp. 1993–2013.
11 Copyright c
2019 by ASME
... This is especially true when using complex design methods (e.g., those generated using algorithms, such as topology optimization), as the final design may not actually be manufacturable without the imposition of the manufacturability constraints. This requires a rigorous process of mapping the useful manufacturing knowledge into design-focused manufacturability constraints; the purpose of these is to limit the design candidates to those which are manufacturable using a particular process or series of processes [55][56][57][58][59][60]. Different design scales must also be considered during this mapping; the basic scales referred to in this paper are defined in Figure 3. . ...
... See #49, Table 4 44. Dimensional error [Layer with shell or contour] See #49, Table 4 45 [8,56,57], the minimum macro-scale feature scale should be 2.5 times the nozzle diameter for any parts which are taller than the part length scale. An exception to this is for thin-walled structures which do not need the same support due to their geometry, which keeps the part stable during printing [102] In order to establish the minimum printed element length needed to securely print on a non-polymer surface, a series or prints were done on a clean, polished glass plate (Figure 13a). ...
Article
Full-text available
This article develops and demonstrates a set of design-focused manufacturability constraints for the fused deposition modeling/fused filament fabrication (FDM/FFF) process. These can be mapped from the basic behavior and process characteristics and formulated in terms of implicit or explicit design constraints. When the FDM/FFF process is explored and examined for its natural limitations and behavior, it can provide a set of manufacturing considerations (advantages, limitations, and best practices). These can be converted into manufacturing constraints, which are practical limits on the ability of the process. Finally, these can be formulated in terms of design–useful manufacturability constraints. Many of the constants and parameters must be determined experimentally for specific materials. The final list of 54 major manufacturability constraints presented in this work will better inform designers considering using FDM/FFF as a manufacturing process, and help guide design decisions. After derivation and presentation of the constraint set, extensive discussion about practical implementation is provided at the end of the paper, including advice about experimentally determining constants and appropriate printing parameters. Finally, three case studies are presented which implement the constraints for simple design problems.
... 15,16 However, the design flexibility provided by AM is not free from constraints. 17 For example, some geometries require the introduction of support structures to reduce residual stresses and distortions. 18,19 Several algorithms have been introduced to overcome AM production constraints in TO, using Solid, Isotropic Material with Penalization (SIMP) or level-set method. ...
... The size of the tetrahedral cells is small enough to consider the lattice beams as selfsupporting, as pointed out by Sola et al. 45 Some of the well-known optimization methods for AM were not considered in this study. For example, this work did not address the overhang constraints defined in Patterson et al. 17 and Jiang et al., 18 as we accepted the presence of structural supports in this study. Further investigations could include this manufacturing-oriented filter. ...
Article
Structural engineering in the automotive industry has moved towards weight reduction and passive safety whilst maintaining a good structural performance. The development of Additive Manufacturing (AM) technologies has boosted design freedom, leading to a wide range of geometries and integrating functionally-graded lattice structures. This paper presents three AM-oriented numerical optimization methods, aimed at optimizing components made of: i) bulk material, ii) a combination of bulk material and graded lattice structures; iii) an integration of solid, lattice and thin-walled structures. The optimization methods were validated by considering the steering column support of a mid-rear engine sports car, involving complex loading conditions and shape. The results of the three methods are compared, and the advantages and disadvantages of the solutions are discussed. The integration between solid, lattice thin-walled structures produced the best results, with a mass reduction of 49.7% with respect to the existing component.
... However, the interpretation of the optimization results is scattered throughout various papers and not yet systematized in the literature. [8][9][10] In this article, a critical appraisal of the results obtained by topology optimization (TO) is presented by modeling a feasible component, such as a front steering upright, by beam, plate, and solid structures. A detailed discussion of the design choices is presented in synergy with the additive manufacturing (AM) process opportunities. ...
Article
This article purposes on developing and on re-interpreting the numerical results of a topology optimization for a structural component built via additive manufacturing. A critical appraisal of the optimization results is presented by modeling the feasible component with a holistic approach that merges structural and manufacturing requirements. The procedure is expected to provide a design guideline for similar applications of practical relevance, toward an increase of the right-first-time parts that is required to bring additive manufacturing to its full competitiveness. Topology optimization of a steering upright for a Formula SAE racing car was performed by targeting weight minimization while complying with severe structural constraints, like global and local stiffness performance. Cornering, bumping and braking vehicle conditions were considered. The optimization constraints were evaluated via finite element analysis on a reference component, where the loading conditions were retrieved from telemetry data. The reference part was manufactured by computer numerical control machining from a solid aluminum block. Spurred by the interpretation of the topology optimization predictions, a new upright geometry was designed and validated by calculating its stress field and the possible occurrence of Euler buckling. The new upright was 9% lighter than the reference component. The new geometry was analyzed according to Design for Additive Manufacturing principles to choose the orientation on the build platform and the supports’ location and geometry. The part was successfully manufactured and proved consistent with the application.
... The minimum radius constraint is implicitly satisfied via the density filter described in the previous section; the filtering radius is set to 0.04 m. This formulation (P1) works particularly well for structural topology optimization, where a volume constraint may represent a cost requirement and the minimum feature size of a structure may be restricted by manufacturing technology (Patterson et al. 2019). Furthermore, the formulation is straightforward to implement and fairly numerically stable when using a SIMP topology optimization approach with the MMA algorithm. ...
Article
Full-text available
This paper presents some practical formulations for heat conduction topology optimization problems. In post-optimization analysis, temperature metrics are often used to compare the performance of optimized structures, yet are not used generally as optimization objectives. In this article, SIMP-based topology optimization is used to explore several objective functions related to electronics applications to demonstrate clearly the impact of improper objective selection. Performance variations over 100% were observed when comparing key metrics between optimized structures. Findings here are extended to problems in electronics domains, where temperature optimization may be used in unconventional ways to capture more realistic design considerations. This includes an investigation in the combinatorial use of objectives and constraints to satisfy electronics requirements. Four case studies are presented where topology optimization methods are used to maximize system performance metrics while satisfying temperature constraints.
Thesis
Full-text available
Doctoral Dissertation: In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.
Article
Full-text available
This paper describes the collection of a large dataset (6930 measurements) on dimensional error in the fused deposition modeling (FDM) additive manufacturing process for full-density parts. Three different print orientations were studied, as well as seven raster angles (0 • , 15 • , 30 • , 45 • , 60 • , 75 • , and 90 •) for the rectilinear infill pattern. All measurements were replicated ten times on ten different samples to ensure a comprehensive dataset. Eleven polymer materials were considered: acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), high-temperature PLA, wood-composite PLA, carbon-fiber-composite PLA, copper-composite PLA, aluminum-composite PLA, high-impact polystyrene (HIPS), polyethylene terephthalate glycol-enhanced (PETG), polycarbonate, and synthetic polyamide (nylon). The samples were ASTM-standard impact-testing samples, since this geometry allows the measurement of error on three different scales; the nominal dimensions were 3.25 mm thick, 63.5 mm long, and 12.7 mm wide. This dataset is intended to give engineers and product designers a basis for judging the accuracy and repeatability of the FDM process for use in manufacturing of end-user products.
Article
Full-text available
Additive manufacturing (AM) has developed rapidly since its inception in the 1980s. AM is perceived as an environmentally friendly and sustainable technology and has already gained a lot of attention globally. The potential freedom of design offered by AM is, however, often limited when printing complex geometries due to an inability to support the stresses inherent within the manufacturing process. Additional support structures are often needed, which leads to material, time and energy waste. Research in support structures is, therefore, of great importance for the future and further improvement of additive manufacturing. This paper aims to review the varied research that has been performed in the area of support structures. Fifty-seven publications regarding support structure optimization are selected and categorized into six groups for discussion. A framework is established in which future research into support structures can be pursued and standardized. By providing a comprehensive review and discussion on support structures, AM can be further improved and developed in terms of support waste in the future, thus, making AM a more sustainable technology.
Article
Full-text available
The direct fabrication of miniaturized polymer components by Additive Manufacturing (AM) processes is a remarkable method at the micro dimensional scale. However, the measurement of complex micro products and the evaluation of the related uncertainty are still particularly challenging and necessary in the micro AM field. In the DTU, a proprietary Vat Photopolymerization machine able to produce micro features has been designed, built and validated. This study evaluates the capability of the machine in terms of printed dimensions and the corresponding uncertainty assessment. For this purpose, two test parts with micro features of different geometries and dimensions have been designed and five samples of each test part have been printed. The dimensions of the micro features have been evaluated for quality control capability assessment and to stablish procedures for verification of AM machines.
Article
Full-text available
A useful and increasingly common additive manufacturing (AM) process is the selective laser melting (SLM) or direct metal laser sintering (DMLS) process. SLM/DMLS can produce full-density metal parts from difficult materials, but it tends to suffer from severe residual stresses introduced during processing. This limits the usefulness and applicability of the process, particularly in the fabrication of parts with delicate overhanging and protruding features. The purpose of this study was to examine the current insight and progress made toward understanding and eliminating the problem in overhanging and protruding structures. To accomplish this, a survey of the literature was undertaken, focusing on process modeling (general, heat transfer, stress and distortion and material models), direct process control (input and environmental control, hardware-in-the-loop monitoring, parameter optimization and post-processing), experiment development (methods for evaluation, optical and mechanical process monitoring, imaging and design-of-experiments), support structure optimization and overhang feature design; approximately 143 published works were examined. The major findings of this study were that a small minority of the literature on SLM/DMLS deals explicitly with the overhanging stress problem, but some fundamental work has been done on the problem. Implications, needs and potential future research directions are discussed in-depth in light of the present review.
Conference Paper
Full-text available
This article addresses the generation and use of manufacturability constraints for design under hybrid additive/subtractive processes. A method for discovering the natural constraints inherent in both additive and subtractive processes is developed; once identified, these guidelines can be converted into mathematical manufacturability constraints to be used in the formulation of design problems. This ability may prove to be useful by enhancing the practicality of designs under realistic hybrid manufacturing conditions, and supporting better integration of classic design-for-manufacturability principles with design and solution methods. A trade-off between design manufacturability and elegance has been noted by many scholars. It is posited that using realistic manufacturing conditions to drive design generation may help manage this trade-off more effectively, focusing exploration efforts on designs that satisfy more comprehensive manufacturability considerations. While this study focuses on two-step AM-SM hybrid processes, the technique extends to other processes, including single-process fabrication. Two case studies are presented here to demonstrate the new constraint generation concept, including formulation of shape and topology optimization problems, comparison of results, and the physical fabrication of hybrid-manufactured products. Ongoing work is aimed at rigorous comparison between candidate constraint generation strategies and the properties of the constraint mapping.
Article
Purpose The purpose of this paper is to introduce the multi-solution nature of topology optimization (TO) as a design tool for additive manufacturing (AM). The sensitivity of topologically optimized parts and manufacturing constraints to the initial starting point of the optimization process leading to structures with equivalent performance is explored. Design/methodology/approach A modified bi-directional evolutionary structural optimization (BESO) code was used as the numerical approach to optimize a cantilever beam problem and reduce the mass by 50 per cent. Several optimized structures with relatively equivalent mechanical performance were generated by changing the initial starting point of the TO algorithm. These optimized structures were manufactured using fused deposition modeling (FDM). The equivalence of strain distribution in FDM parts was tested with the digital image correlation (DIC) technique and compared with that from the modified BESO code. Findings The results confirm that TO could lead to a wide variety of non-unique solutions based on loading and manufacturability constraints. The modified BESO code was able to reduce the support structure needed to build the simple two-dimensional cantilever beam by 15 per cent while keeping the mechanical performance at the same level. Originality/value The originality of this paper lies in introduction and application of the multi-solution nature of TO for AM as a design tool for optimizing structures with minimized features in the overhang condition and the need for support structures.
Conference Paper
Geometric tolerances for new products are sometimes assigned without specific knowledge of the cost or feasibility of manufacturing them to the assigned tolerances, which can significantly drive up production costs and lead to delays and design revisions. We present an interactive tool that quickly estimates the manufacturability of assigned tolerances for additive manufacturing and a compact visualization to present this information to the designer. The designer can use the system to explore feasible build orientations and then adjust specified tolerance limits if all tolerances are not simultaneously achievable at a single orientation. After the designer is satisfied that the range of feasible orientations has been fully explored, a physical programming approach is used to identify a single orientation to best satisfy the designer’s preferences. The calculation and visualization of the results is done in real-time, enabling quick iteration. A test case is presented to illustrate the use of the tool.
Article
Design for additive manufacturing (DFAM) guidelines are important for helping designers avoid iterations and leverage the design freedoms afforded by additive manufacturing (AM). Comprehensive design guidelines should incorporate a variety of features of interest to designers, and given the wide variety of AM processes and their associated capabilities and limitations, those guidelines may need to be process- or even machine-specific. One way to generate detailed DFAM guidelines is to implement a metrology study focused on a strategically designed test part. This paper describes how quantitative design guidelines are compiled for a polymer selective laser sintering (SLS) process via a metrology study. As part of the metrology study, a test part is designed to focus specifically on geometric resolution and accuracy of the polymer SLS process. The test part is compact, allowing it to be easily inserted into existing SLS builds and therefore eliminating the need for dedicated metrology builds. To build a statistical foundation upon which design guidelines can be compiled, multiple copies of the test part are fabricated within existing commercial builds in a factorial study with materials, build orientations, and locations within the build chamber as control factors. Design guidelines are established by measuring and analyzing the as-built test parts. The guidelines are summarized in this paper and documented in a publicly accessible, online web tool.
Article
In the field of manufacturing process planning and initial operation of machines, machine parameters are often provided from few either expensive and time-consuming experiments or faster but less accurate numerical simulations. Another option is to use machine learning to predict process qualities based on machine parameters. Thereby, transfer learning can overcome the gap between real and simulation data. We evaluated two different approaches based on artificial neural networks, namely soft-start and random initialization, in a real injection molding process. The results show better learning rates and predictions that are more accurate while using fewer experimental data.