Input: In conclusion, the complexity that AM allows makes it possible to manufacture true biomimetic structures, yet, knowledge of the biological structure is necessary.

complexity

manufacture

biomimetic

AM

biological structure


Input: 3 Customized and freeform design Traditional design for engineering involves individual part design in computer-aided design tools, with engineering expertise and intuition required to understand the limits of traditional subtractive manufacturing.

Customized and freeform design

design

design

engineering

engineering

computer-aided design

limits

traditional subtractive manufacturing


Input: This most often results in traditional designs with right angles and flat surfaces due to the simplicity for subtractive manufacturing of such designs.

designs

designs

surfaces

subtractive manufacturing


Input: Over the last few years, advanced manufacturing techniques have become available and viable which allows the design engineer more freedom to create parts with more complex designs.

manufacturing

design

designs


Input: These new design capabilities allow organic shapes and freeform designs, which are often also termed biomimetic due to their organic shapes resembling natural structures and sometimes, in the case of medical devices in particular, the forms are shaped to fit natural materials such as bone implants.

design

freeform designs

biomimetic

fit

materials

medical devices

implants

as


Input: Natural structures tend to comprise of curves and organic shapes as they represent a balance between minimal energy expenditure and material used on the one hand, and maximal return of work on the other hand, all within the organismdevelopmental limits.

as

material

energy expenditure

limits


Input: Freeform and custom designs may be termed biomimetic as they resemble natural structures in these aspects, but without the constraints imposed by the organism itself.

Freeform

biomimetic

designs

be

as


Input: Despite this freedom of design, traditional engineering thinking is often limited to experience of using right angles and flat surfaces.

design

engineering

surfaces


Input: In order to optimally use this new design freedom, additional tools are needed.

design freedom

tools


Input: The most important contributing tools for freeform design are discussed here.

tools

freeform design


Input: One of these is the shaping of curved and organic surfaces by the use of T-splines and more recently polygonal non-uniform rational B-spline.

shaping

surfaces

non-uniform rational B-spline

T-splines


Input: These tools allow organic designs with curved surfaces that often resemble natural structures.

tools

designs

curved surfaces


Input: These are also critical tools in final steps of topology optimization and even true biomimetic reverse engineering structures, ultimately allowing for watertight models with curved geometries.

tools

topology optimization

biomimetic

watertight models

geometries

engineering


Input: Not only do these tools make custom curved shapes possible in a relevant workspace, but they are also effectively translated into geometries suitable for simulation and/or AM.

tools

geometries

simulation

AM


Input: In terms of custom design especially for implants patient-specific implants are a special category and require a particular workflow involving the processing of medical image data, the use of CAD tools and design for AM knowledge to yield a good resulting implant as discussed in.

design

design

implants

implants

medical

implant

workflow

image data

CAD

AM

as


Input: An example is shown in 4 where a patient-specific facial implant was produced in Ti6Al4V.

implant


Input: Increases in computing power, the availability of cloud computing and the wider availability of CAE tools all led to the sharp increase in advanced and complex design capability.

power

cloud computing

CAE

led

design


Input: One of the first examples was the Insight Plotfrom Solidworks, which demonstrates the main load paths in a designed part, as calculated from one or more applied loads by finite element modelling.

designed

as

finite element modelling


Input: This was a forerunner of topology optimization tools which will be discussed in more detail in the next section.

topology optimization

tools

be


Input: As mentioned above, AM releases much of the traditional limits of subtractive manufacturing allowing much wider allowed manufacturing complexity.

As

AM

subtractive manufacturing

manufacturing

limits

complexity


Input: This is already broadly acknowledged, and new design rules for reliable manufacturing in all forms of AM are emerging and in many cases are already mature and well-defined.

design rules

manufacturing

AM


Input: The design for AM rules and practical issues are discussed in detail in and more recently in the context of topology optimization in.

design

topology optimization

AM


Input: One major advantage of these new design tools for creating manually organic and curved surfaces and shapes is the ability to create artistic features the resemblance to biological/natural and organic structures brings a new dimension to artistic designs for end-use products.

design

organic structures

dimension

designs

curved surfaces

biological/natural


Input: The use of 3D printing in arts, fashion and jewelry is growing as is shown in and artistic design is easily achieved by AM, without significantly adding to the cost of the product.

3D printing

AM

fashion

as

design


Input: Freeform design tools can be used to shape custom-fit sportswear or footwear, with the first fully-AM footwear being produced by Adidas Futurecraft.

Freeform design

be

footwear

footwear


Input: The design of this shoe is entirely latticed giving a futuristic and biomimetic visual appeal.

design

biomimetic


Input: These are two examples of mass production and mass customization by AM.

mass production

AM


Input: Aspects of importance besides personal/custom design for fit, is the incorporation of logos or names, and the ability for the customer to take part in the design process giving them some options making their product unique.

design

fit

design process


Input: 4 Simulation-driven biomimetic design One of the first drivers of the concepts behind simulation-driven design was from the ideas of Julius Wolff, the 19th Century Orthopedic surgeon, who first suggested that, a consequence of primary shape variations and continuous loading, or even due to loading alone, bone changes its inner architecture according to mathematical rules and, as a secondary effect and governed by the same mathematical rules, also changes its shape.

Simulation-driven

simulation-driven

biomimetic design

design

variations

mathematical

mathematical

bone

architecture

as


Input: The concept of topology optimization sprung from here from the concept that a structure can be optimized by following load paths and be modified to fit the particular mechanical requirement.

topology optimization

structure

fit

be

be

mechanical


Input: The first industrial class software solutions incorporating the rules of design along with the ability to capture the along with the constraints to automatically generate design was released in the early 1990This was primarily the beginning of CAE simulation driving inspirational designs.

industrial

software

design

design

designs

CAE simulation


Input: Over the years many manufacturing constraints have also been added to shape these designs to be cognizant of the downstream manufacturing, and is relevant to different manufacturing processes.

manufacturing constraints

designs

be

manufacturing

manufacturing processes


Input: If the part is produced by an extrusion process, then using the extrusion constraints will generate a shape that is extrudable across the defined design space.

extrusion process

extrusion

design space


Input: Likewise, on specifying a casting constraint, the bionic shape that is generated will be free of undercuts for easy extraction from the casting molds.

casting

be

undercuts

casting molds


Input: For AM, overhang constraints generate shapes that have minimal support requirement during build in a given print direction, with less horizontal sections, for example.

AM

print

overhang constraints

build

support


Input: There are various manufacturing constraints in AM that can be incorporated into the design optimization process and it is the incorporation of these into the topology optimization process which will create designs ready for production.

manufacturing constraints

design optimization process

AM

production

be

topology optimization process

designs


Input: Shown in 5 are selected examples from Altair covering a variety of parts that illustrate the power of simulation in mimicking nature for product design that outperform conventional designs and are manufacturable and lightweight.

power

simulation

product design

designs

manufacturable

lightweight


Input: The first example is the HardMarque automotive piston which was designed and optimized for production by additive manufacturing in titanium the end result is reported to be 25% lighter and equally strong compared to the original aluminium part.

automotive

designed

production

additive manufacturing

titanium

be

aluminium


Input: The second example from Renishaw is a seat post bracket of a mountain bike, meant to replace a cast aluminium part with additively manufactured titanium: the mass reduction was reported at 40%.

bracket

cast

additively manufactured

aluminium

reduction


Input: The third example is a case study from the aerospace industry, in particular the optimization of a mechanical hinge for an Airbus A320 by the European Aeronautic Defence and Space Innovation Works in this case a 75% mass reduction was realized.

case study

optimization

reduction

aerospace industry

mechanical

Airbus A320


Input: The last example is a research project with Laser Zentrum Nord focusing on lightweighting of aircraft cabin brackets.

research

Laser

lightweighting


Input: Simulation-driven design in the context of AM refers to the use of simulation to numerically and a given space to meet some desired performance criteria under a defined set of constraints.

Simulation-driven

simulation

design

AM

performance

set


Input: This currently refers to either of topology optimization or generative design, which can often be used interchangeably in the context of AM both involve the use of simulation.

topology optimization

generative design

simulation

be

AM


Input: Topology optimization refers to optimizing an existing shape or design space.

Topology optimization

design space


Input: Generative design is a broader definition of exploring a variety of possible designs within a given space with a desire to identify an optimal solution from various possible solutions meeting the same performance criteria.

Generative design

designs

solution

performance


Input: In the context of design for AM, both the approaches are aimed at creating light-weight parts which mostly contain material in areas were load is experienced and material is removed in areas which do not require it.

design

AM

light-weight

material

material

areas

areas


Input: This process of simulation and material-removal or addition is repeated iteratively until an optimization goal is achieved, and this iterative process may be seen analogous to the process of evolution.

process

optimization

process

process

evolution

simulation

be


Input: In fact, these simulations sometimes make use of genetic and evolutionary algorithms.

simulations

genetic and evolutionary algorithms


Input: Effectively, these algorithms incorporate rules like in nature to mathematically disallow weaklings to proliferate, but in an accelerated fashion using clever computational methods.

algorithms

fashion

clever computational methods


Input: More recently this was also described in terms of manufacturing challenges in and in terms of available software tools and their differences and limits in.

manufacturing

software

limits


Input: A good example of topology optimization, applied to an extreme lightweighting requirement is the design of a titanium alloy drone frame, with larger dimensions than can be produced on typical powder bed fusion systems.

topology optimization

design

dimensions

lightweighting

titanium alloy

be

powder bed fusion


Input: This was produced on the large-scale laser powder bed fusion system called Aeroswift and the design done in collaboration with Altair.

laser powder bed fusion system

design


Input: The design iteration process is shown in 6, done in Altair Inspire.

design

process


Input: Another example of a topology optimized part a load bearing bracket is shown in 7, which is taken from.

topology

bearing bracket


Input: This titanium alloy bracket was designed to replace a traditional composite bracket in an experimental vehicle for the Shell eco-challenge.

titanium alloy

composite

bracket

bracket

Shell

designed

experimental


Input: The design process schematic here shows the original composite part, the design space, the optimized solution and the final smoothed solution, after application of connections and polyNURBS to the surfaces.

design process

design space

solution

smoothed solution

surfaces

composite

polyNURBS


Input: This part was also used in a round robin test whereby the same bracket was produced at various commercial laser powder bed fusion systems and detailed analysis performed using microCT.

round robin test

microCT

bracket

laser powder bed fusion systems


Input: The study highlighted the need for testing AM parts to ensure structural integrity.

testing

AM parts

structural integrity


Input: Another example is the design for a large bracket for the same vehicle related to the above-mentioned example.

design

bracket


Input: 8 shows the optimized topology itself which is also latticed: this is a sequential process in most software packages and the area to be latticed and the lattice parameters are selected by the user.

topology

process

software

lattice

area

be


Input: Latticing will be discussed in the next section and holds many advantages but must be carefully implemented in a design, due to issues such as requirement for supports inside the lattice region, and struts which are potentially too thin.

be

be

as

design

supports

lattice

struts


Input: The first is the Bugatti brake caliper which is shown in 9, and which is currently the worldlargest functional part produced in titanium by AM.

caliper

titanium

AM


Input: In this case the use of Ti6Al4V titanium alloy is especially useful for light-weighting, as this material is already strong and relatively light.

alloy

as

material


Input: Its use for automotive and aerospace applications is well known, but manufacturing complex designs by traditional manufacturing methods in this material is extremely challenging.

automotive

aerospace

manufacturing

traditional manufacturing

designs

material


Input: The topology optimization result is visually impressive, the performance of this caliper has been validated in various tests and is used in production vehicles, with a 40% mass reduction compared to the previous version made of aluminium.

topology optimization

performance

reduction

caliper

production

aluminium


Input: 5 Cellular and lattice design Cellular structures exist in nature in numerous shapes, sizes and packing arrangements some of the most well-known examples are the beehoneycomb, wood cells and spongy bone, all of which are discussed in a book by Gibson.

lattice design

Cellular structures

beehoneycomb

wood cells

spongy bone


Input: MicroCT scans of some natural cellular materials are shown in 11.

MicroCT

cellular materials


Input: In fact, one of the first true observations of cellular structures in nature can be traced back to 1665, when Robert Hooke published his observation of the cellularity in cork and suggested that the unique behavior of cork was attributable to its underlying cellular structure.

cellular structures

cellular structure

be

cork

cork


Input: Humans have been using cellular materials such as wood, cork and bamboo, several millennia before we realized the underlying structural basis for their interesting behaviour.

cellular materials

as

cork


Input: Lattices today owe much of their origins and design selection to mathematics and crystallography, as well as following Maxwellstability criterion, which was primarily developed in the context of large engineering structures.

Lattices

design

crystallography

as

as

engineering


Input: The main utility of cellular or lattice structures lies in their ability to meet performance targets while enabling significant mass reduction, something that is a principle commonly embodied in nature.

lattice structures

performance

reduction


Input: While cellular materials do tend to have lower effective material stiffness and strength properties, this reduction is often acceptable and can be tailored to the application, as well as varied locally.

cellular materials

be

as

as

effective material stiffness

strength properties

reduction


Input: Lattices may also be useful for other purposes besides light-weighting: they have interesting thermal, acoustic properties and energy absorbing properties under compressive loading they perform a crucial protection role in nature.

Lattices

properties

properties

be

compressive loading


Input: Cellular materials have also been seen as a crucial enabler for large system-level multi-functional design optimization, such as in an aircraft wing.

Cellular materials

as

as

multi-functional design optimization

aircraft wing


Input: The categorization of natural cellular structures is discussed in more detail in a recent review article which focuses on biomimetic design of cellular materials utilizing cellular designs in engineering systems.

cellular structures

biomimetic design

cellular designs

cellular materials

engineering


Input: Perhaps the most commonly used, and well-known bio-inspired cellular material is the honeycomb, which has found a wide range of applications in architecture, transportation, chemical engineering and more, as compiled in a review article.

bio-inspired

honeycomb

material

as

range

architecture

engineering


Input: With regard to additively manufactured cellular materials, the emphasis in the past decade has been on lattice structures, and their use for medical bone-replacement implant applications.

additively manufactured

materials

lattice structures

medical

implant


Input: In this application, the primary role of the lattice is to allow for osseointegration of bone into the implant, thereby causing better fixation.

lattice

osseointegration

bone

implant


Input: A recent book chapter describes the most important criteria for bone regeneration in titanium implants produced by powder bed fusion and the production of topologically designed and otherwise designed porous lattices for this application was also reviewed in.

bone regeneration

topologically

lattices

titanium implants

powder bed fusion

production

designed

designed


Input: From an engineering standpoint, cellular materials are realized practically in commercial software packages using different approaches.

engineering

cellular materials

software


Input: Traditional CAD software uses mesh-based representation, but recent developments in software are exploring the use of volumetric object representation to generate surfaces, and in at least one case, the use of implicit modeling via the definition of fields that then generate cellular structures.

CAD

modeling

software

surfaces

cellular structures


Input: Mesh-based approaches can generate visually impressive lattices which conform well to the original surface design, and is relatively easily implemented for complex part geometries.

lattices

surface

geometries

design


Input: The volumetric object representation approach allows for the user to select a unit cell from a wider variety of cellular designs.

unit cell

cellular designs


Input: The repeated unit cell approach also allows relatively easy prediction of mechanical properties of the structure, easing the design process.

unit cell

prediction

mechanical properties

structure

design process


Input: A series of unit cells and corresponding repeated lattice structures are shown in 12.

unit cells

lattice structures


Input: These are all designed with the same total density, but the different designs result in different minimum feature thickness and pore sizes.

designed

designs

minimum feature thickness

density

pore sizes


Input: The first four are strut-based and the next four are minimal surface designs.

strut-based

designs

surface


Input: The latter are found in nature, and have been shown to have good properties for bone implant applications.

properties

bone implant applications


Input: These minimal surfaces are sheet-based designs which are often self-supporting and tend to have zero average curvature at every point on the surface, which makes for a more even distribution of stresses within these structures.

surfaces

surface

distribution

designs

self-supporting

zero average curvature


Input: Despite the growing prevalence of design software capable of generating cellular structure designs, it is not always apparent what the best unit cell for a specific application is and this becomes even more challenging in the context of multi-functional design.

design

cellular structure

design

unit cell


Input: It is in such a context that biomimetic design can play a key role, in helping develop structure-function relationships based on observations of cellular materials in nature, and using these to guide selection of cellular materials.

biomimetic design

cellular materials

cellular materials


Input: Natural cellular materials span the range of parameter space used in design, from beam or strut-based materials to surface based ones, including structures that combine both types, as shown in 13.

cellular materials

as

range

parameter

materials

surface

design

strut-based

beam


Input: These cellular materials occur in nature both internal to a form, as well as externally on the surface.

cellular materials

as

as

surface


Input: The main application of lattice structures, which has resulted in considerable research efforts, is their use in medical implants.

lattice structures

research

medical implants


Input: For this application the pore sizes required are typically small, requiring small feature sizes in general.

pore sizes

feature sizes


Input: Other applications than medical, such as in light-weight structures for aerospace or automotive parts, might prefer thicker lattices to focus on mechanical reliability and strength.

medical

aerospace

automotive

as

light-weight

strength

lattices

mechanical reliability


Input: Experimental work with lattices with thick struts show excellent strength properties as shown in for 50% density Ti6Al4V lattices of two strut-based designs.

Experimental

lattices

lattices

struts

strength properties

density

as

strut-based designs


Input: Simple strut-based lattice designs can be classified according to the Maxwell criterion as either bending-dominated or stretch-dominated as illustrated schematically and by a few examples in 14.

Simple

lattice designs

be

as

as

Maxwell criterion


Input: The Maxwell criterion for simple strut-based 3D structures is: M = b 3j + 6 Where b = the number of struts, and j = the number of joints When M < 0 the structure is bending-dominated When M 0 the structure is stretch-dominated and When M > 0 the structure is over-rigid Bending-dominated refers to the struts which tend to bend under compression of the lattice resulting in shear failure, while stretch-dominated structures are stiffer and fail in a layer-by-layer mechanism.

Maxwell criterion

3D structures

structure

structure

structure

lattice

layer-by-layer

simple

b

b

struts

struts

compression

shear failure

stretch-dominated structures


Input: These failure modes have been observed in relatively thick-strut lattices and imaged by microCT in their initial failure locations.

failure modes

lattices

failure

microCT


Input: The mechanical response of lattice structures in general follows a linear elastic response up to the first point of buckling or failure, followed by a plateau region, followed by final densification.

mechanical response

failure

lattice structures

elastic

first point of buckling

densification


Input: This is shown in the example in 15, which also shows why cellular materials are useful for energy absorption as they can handle significant yielding without catastrophic failure, under most circumstances.

cellular materials

as

energy absorption

failure


Input: A lattice structure can be approximated as an open-cell foam, with effective elastic modulus E of the lattice related to the density of the structure and the elastic modulus of the bulk material-solid as follows: E = 2 E s o l i d s o l i d 2 In this relationship, the constant depends on the manufacturing accuracy and material properties and varies between 0.1 and 4 but is a constant for a specific material and process.

lattice structure

be

as

foam

material

as

s

o

s

o

specific material

elastic modulus

density

elastic modulus

lattice

structure

material properties

process

manufacturing

accuracy


Input: What this relationship shows is that the effective elastic modulus can be controlled by the density alone this means that a lattice with unit cell design of 50% density may use any unit cell size as long as the total space filled contains at least six unit cells in each direction then the material stiffness will be the same.

elastic modulus

density

density

be

as

as

be

lattice

unit cell

unit cell

unit cells

design

material stiffness


Input: This means lattices with many thin struts might perform the same as lattices with less thick struts, an interesting design aspect which can be varied by application requirement.

lattices

struts

struts

as

be

design


Input: It is also important to note that the exponent refers to ideal bending-dominated lattice while an ideal stretch-dominated lattice has exponent This is illustrated in 16, for a range of lattice types clearly this exponent may vary somewhat depending on the lattice design selected.

lattice

lattice

lattice

range

lattice design


Input: Besides the relationships mentioned above, lattice designs must also be considered relative to manufacturing limits.

lattice designs

be

manufacturing

limits


Input: For example, sheet-based designs can typically print without supports, and strut-based designs can print without supports up to a certain strut length for horizontal struts.

designs

strut-based designs

certain strut length

horizontal struts

print

print

supports

supports


Input: Therefore, manufacturing constraints are imposed on the design possibilities.

manufacturing constraints

design


Input: The most important limits are the minimum feature size, which, in practice, is limited not only by the powder size and laser spot size, but also by the 3D model slicing accuracy and the resulting hatch and contour scanning employed.

limits

minimum feature size

laser spot size

contour scanning

powder

3D model slicing


Input: For example, in a recent study of thin-strut lattices, the standard processing parameters resulted in the inability to produce struts varying gradually from 0.2 to 0.4 mm.

lattices

standard

parameters

struts

mm


Input: Here, different designs were produced with approximately the same strut dimensions despite differences in design.

designs

dimensions

design

strut


Input: These thin-strut lattices also have relatively large surface roughness values compared to the strut thickness, which understandably affects the mechanical properties more than would be expected for a thicker-strut version.

lattices

mechanical properties

surface roughness

strut thickness

be


Input: In this above-mentioned study the experimental elastic modulus values were significantly lower than predicted mostly attributed to surface roughness and irregularity which creates stress concentrations in notches and in locations of very thin wall thickness.

experimental

predicted

elastic modulus

surface roughness

stress concentrations

notches

wall thickness


Input: Effectively for a metal laser powder bed fusion system with about 100 spot size, the minimum reliable wall thickness should be 0.3-0.4 mm if no special precautions or optimization for strut manufacturing is done to enhance the manufacturability.

metal

be

laser powder bed fusion system

strut

spot size

wall thickness

mm

manufacturing

optimization

manufacturability


Input: The next section discusses material properties and will specifically mention limits with regards to lattice manufacturability.

material properties

limits

lattice


Input: 6 Material properties of AM biomimetic parts Biomimetic-designed and produced parts are visually so vastly different from traditional manufactured parts, that it causes mistrust and resistance to acceptance of this new technology, especially by engineers.

Material properties

manufactured

technology

AM

resistance


Input: In some ways this is to be expected, as AM has a history of over-hype and under-delivery in the past.

be

as

AM


Input: In the qualification process, mechanical properties of the optimized process can be tested and validated as demonstrated for Ti6Al4V in.

process

mechanical properties

process

be

as


Input: In order to obtain defect-free and accurately produced parts, X-ray tomography can be used as outlined in.

accurately

X-ray tomography

be

as


Input: The specific process parameters which combine to create an object in AM all have an influence on the subsequent material properties and the manufacturing process of the object as a whole.

process parameters

material properties

AM

manufacturing process

as


Input: This is true not only for fully dense objects, but also for complex or lattice design with biomimetic features such as custom or complex shapes, inner structures or surface modifications.

fully dense

lattice design

biomimetic features

as

complex shapes

surface modifications


Input: In this case, material properties and the properties of i.e.

material properties

properties


Input: single building blocks and joints between them also influence the properties of the LPBF object.

properties

LPBF


Input: Defects and flaws such as porosity occurs in the LPBF process due to various reasons and this can influence the mechanical properties of the final parts.

Defects

flaws

mechanical properties

as

LPBF


Input: There are many process parameters the laser power, laser spot size and scanning speed, hatch distance, material properties, powder particle size distribution and powder layer thickness, the strategy, design and orientation of the 3D part and its supports, the scanning and building strategy, etc.

process parameters

material properties

distribution

orientation

scanning

laser power

laser spot size

scanning speed

hatch distance

layer thickness

powder particle

powder

design

3D part

supports


Input: which all may influence the molten pool size, further solidification, microstructural grain growth and eventually the mechanical properties, lifetime and performance of LPBF parts.

molten pool

solidification

microstructural

grain growth

mechanical properties

performance

LPBF


Input: The details of the AM process are discussed in the comprehensive review paper.

AM process


Input: It is already well known that variation of process parameters may influence the formation of porosity and may lead to extensive flaws and build imperfections, as is shown for example in a round robin test recently.

variation

process parameters

flaws

porosity

lead

as

build

round robin test


Input: This highlights the need for process optimization.

process optimization


Input: Other properties such as corrosion are also strongly affected by processing conditions and are important for biomimetic applications, especially medical applications.

properties

as

biomimetic applications

medical applications


Input: For example, it was shown that a higher corrosion resistance of Co-Cr dental alloy was obtained by Selective Laser Melting in comparison with the Selective Laser Sintering process, due to a passive oxide protecting layer which formed on the surface of the SLM sample.

corrosion resistance

surface

sample

Co-Cr dental alloy

oxide

Selective Laser Melting

Selective Laser Sintering process

SLM

layer


Input: Takaichi found that metal elution from the LPBF dental implants was smaller than that of the as-cast Co-Cr alloy.

metal

alloy

LPBF

dental


Input: Thus, it could be said that LPBF materials have superior corrosion properties.

be

LPBF materials

corrosion properties


Input: However, process-parameters can influence the corrosion behavior of samples produced with different process-parameters.

corrosion behavior

samples


Input: It is already known that the level of microporosity affects the corrosion behavior as shown in.

microporosity

corrosion behavior

as


Input: Micro-segregation of elements under specific LPBF process-parameters can occur causing different corrosion behavior at materials processed under different parameters.

Micro-segregation

materials

parameters

elements

LPBF

corrosion behavior


Input: Since melt pool boundaries may differ in corrosion resistance compared to the center of the meltpool, more melt pool boundaries imply different corrosion resistance of LPBF material.

melt pool boundaries

corrosion resistance

melt pool boundaries

corrosion resistance

meltpool

LPBF material


Input: These statements have to be taken into account especially for smart AM advanced biodegradable implants that should degrade with spatial and temporal controllability to meet the requirements of different bone regeneration stages.

be

AM

implants

bone regeneration


Input: LPBF samples have varying surface roughness on side, top and bottom surfaces.

LPBF

surface roughness

surfaces


Input: Attached powder particles can be eliminated by post-process mechanical or chemical procedures.

powder particles

be

post-process

mechanical


Input: However, for LPBF parts with complex shapes and fine features or lattice structures, full powder evacuation and targeted accuracy and roughness values can be quite difficult to obtain.

LPBF

complex shapes

roughness values

lattice structures

powder

be

accuracy


Input: The surface roughness is dependent on the building and scanning strategy, material properties, powder size, layer thickness, etc.

surface roughness

scanning strategy

material properties

powder

layer thickness


Input: This can influence not only the mechanical properties but also the biological response of bone cells or soft tissues when such an object is implanted.

mechanical properties

bone cells

implanted


Input: Moreover, there is currently no general approach and agreement about preferred roughness values or surface micron-scale features and pore size for effective bone cell growth and functioning of implants.

roughness values

surface

micron-scale

pore size

bone cell

implants


Input: For lattice structures, the geometrical characteristics of unit cells, the building direction, overhang angles, hatch and contour scanning strategy may all influence the obtained roughness in the scaffolds and may cause deviations from designed sizes.

lattice structures

scaffolds

designed

unit cells

building direction

overhang angles

contour scanning

roughness


Input: For example, in du Plessis, the elemental cubic lattice was designed with a total 15 mm width, 0.75 mm strut thickness and 8 struts across one direction in total, resulting in 1.28 mm distance between struts and total 65% porosity.

lattice

designed

mm

mm

mm

struts

struts

porosity


Input: One set of samples was built at standard process-parameters recommended for EOS Ti6Al4V in vertical direction, other ones at 45angle.

set

EOS

samples

standard

vertical


Input: Samples were heat-treated for stress-relieving as indicated in.

Samples

heat-treated

as


Input: The differences in strut thickness, roughness and microstructure is clearly visible by cross-sections and also different columnar prior beta-grain orientations are clearly present.

strut thickness

roughness

microstructure

cross-sections

orientations


Input: Samples that were produced at 45 degrees, had 25% lower ultimate compression strength in comparison with vertical samples.

Samples

vertical samples

compression strength


Input: Bending and stretch-dominated lattices fail respectively in shear and layer-by-layer failure modes, and this might depend somewhat on the material ductility.

Bending

lattices

layer-by-layer

failure modes

ductility

material


Input: For a brittle material, shear failure is not desirable and layer-by-layer can be much preferred and even might act as protective mechanism.

brittle material

be

as

shear failure

layer-by-layer

mechanism


Input: The layer-by-layer mechanism is more predictable as it is known where the next failure will occur.

layer-by-layer

predictable

failure

as


Input: In general, manufacturing imperfections might affect thin features more than thick features, hence thin struts should be thickened or well-designed with sufficient safety margin.

manufacturing imperfections

safety

struts

be


Input: The obtained texturization in LPBF materials-grain and sub-grain sizes-depend on the process-parameters used and scanning strategy in LPBF materials as shown by.

LPBF materials

LPBF materials

as

grain

scanning strategy

sub-grain sizes


Input: The microstructure of LPBF solid samples and their mechanical properties, fracture and fatigue behavior have some peculiarities in as-built and heat-treated AM parts, which have been widely studied.

microstructure

samples

mechanical properties

fracture

LPBF

heat-treated

fatigue

AM parts


Input: For example, the columnar boundaries of prior beta-phase were observed in as-built Ti6Al4V ELI specimens and remain even after heat treatment of 950 for 2 hours.

boundaries

heat treatment


Input: Anisotropy in AM is often mentioned.

Anisotropy

AM


Input: For example, the mechanical properties of LPBF Ti6Al4V ELI was found to be strongly anisotropic where three-point bending fatigue tests were used with parts produced in different orientations.

mechanical properties

orientations

LPBF

be

anisotropic

three-point bending fatigue tests


Input: The crack propagation rate and fatigue life of as-built and heat-treated samples correlated with column boundaries and orientation of the layers, i.e.

crack propagation rate

correlated

orientation

fatigue life

heat-treated

column boundaries


Input: correlated with the building direction.

correlated

building direction


Input: For static tensile tests, lower ductility was observed experimentally for the horizontal specimens in comparison with vertical samples this could be attributed to long prior beta-grain boundaries in Ti6Al4V which grow in the build direction and are hence perpendicular to the loading direction in horizontal specimens.

tensile tests

ductility

horizontal specimens

horizontal specimens

vertical samples

be

boundaries

build direction


Input: As it was noted in, the orientation dependency of the ductility in AM is not yet clear and further in-depth investigations need to be done.

As

be

orientation

ductility

AM


Input: Mechanical properties are dependent on building and scanning strategies and these vary for different materials.

Mechanical properties

scanning strategies

materials


Input: For example, LPBF 316 L stainless steel had maximum strength and Youngmodulus under a 45 degree offset between the layer and loading direction, whereas AlSi10Mg revealed the lowest strength in this case.

LPBF

stainless steel

strength

Youngmodulus

strength

offset

layer


Input: In samples manufactured by LPBF from a nickel-based alloy, strong crystallographic texture resulting in anisotropic properties was found in creep behavior: specimens with loading parallel to the building direction were superior compared to specimens with loading axis normal to the building direction.

samples manufactured

LPBF

nickel-based alloy

texture

anisotropic

creep behavior

building direction

building direction


Input: The Young's modulus determined in measurements at room and elevated temperature was different during tensile testing parallel or perpendicular to the building direction.

temperature

building direction

tensile testing


Input: The building direction and laser scanning direction/scanning strategy are important for the mechanical integrity and this adds complexity to the optimal processing protocol for parts of complex shape.

building direction

laser

scanning strategy

complexity

protocol

mechanical integrity

complex shape


Input: Material type, particle size distribution and particle shape, process-parameters, protective atmosphere, building and scanning strategies, post-processing, etc.

Material

particle size distribution

particle

scanning strategies

post-processing


Input: should all be optimized according to the specific LPBF process so that biomimetic objects can be produced with the desired properties.

be

be

LPBF

biomimetic objects

properties


Input: Once material properties and structural integrity have been assessed, the parts produced can be trusted, especially when suitable design safety margins have been incorporated.

material properties

structural integrity

be

design


Input: There are some general suggested guidelines based on the experiences of the authors which can be used in addition to ensure safety and reliability of biomimetic parts in real world applications: 1 For lattices, thin struts might contain micro-porosity, rough surfaces and manufacturing imperfections which affect the mechanical properties sometimes more strongly than thicker features.

be

safety

biomimetic

lattices

surfaces

manufacturing imperfections

mechanical properties

reliability

struts


Input: It was found that the cyclic response of lattices depend not only on the type of bulk material, but also on the roughness of the outer surface of the struts and the distribution of the micro-pores inside the struts which can both affect the crack initiation and crack propagation.

lattices

surface

distribution

crack propagation

material

roughness

struts

struts


Input: Post processing chemical cleaning to decrease strut roughness can be used to minimize this.

Post processing chemical cleaning

strut

roughness

be


Input: The accuracy of various AM techniques are different since different laser spot size, powder layer thickness, process-parameters as well as powder material are used.

accuracy

AM techniques

laser spot size

layer thickness

powder

as

as

material


Input: Therefore, for a particular purpose where mechanical properties are critical, AM lattices should be tested stringently.

mechanical properties

AM

be


Input: To improve mechanical performance of lattice structures for load bearing applications they must be well-designed.

mechanical

lattice structures

be


Input: Van Bael showed that stiffness and compressive strength of lattice structures correlate well with volume fraction.

stiffness

compressive strength

lattice structures

volume fraction


Input: Contuzzi proposed to use solid reinforcements in fine lattice structures that increase load carrying capability of the structure almost linearly with the number of the reinforcements.

lattice structures

structure


Input: Bobbert proposed to use in these applications continuous sheet-based porous structures because they are expected to be less sensitive to such imperfections than beam-based porous structures, to improve fatigue resistance.

porous

porous

fatigue

be

imperfections


Input: 2 For lattices, selecting lattice parameters to ensure no supports are required on the lattice or inside the lattice area is critical.

lattices

lattice

lattice

lattice

supports

area


Input: Here, strut angles and/or length is important.

strut


Input: 3 For irregular geometries from topology optimization and freeform design, it is advisable to perform build-simulation to ensure no local heat accumulation occurs which might lead to residual stress and warping.

geometries

warping

topology optimization

freeform design

heat accumulation

residual stress

lead


Input: In this process, the optimal build angle and supports should be selected.

process

build

supports

be


Input: 4 Residual stress can be minimized by design as mentioned above, and can be further improved by stress-relief heat treatment a relatively simple recommended solution.

Residual stress

be

as

be

design

heat treatment

simple

solution


Input: Heat treatment can have a decisive role on higher ductility and load bearing capacity of lattice structures and might increase fatigue life.

Heat treatment

ductility

fatigue life

capacity

lattice structures


Input: 5 Special attention must be given to the loading direction during use, because anisotropic mechanical properties of LPBF objects exists.

be

anisotropic

properties

LPBF


Input: This anisotropy might not only result from the material and its specific microstructure, but also from scanning and building strategies used for LPBF manufacturing, which might vary with different systems.

anisotropy

material

microstructure

scanning

LPBF


Input: Lattice structures built in different directions have non-identical mechanical properties.

Lattice structures

mechanical properties


Input: 7 Challenges in biomimetic AM Despite all the potential for biomimicry in AM in its various forms, there are some challenges to its practical implementation.

biomimetic AM

biomimicry

AM


Input: Most importantly, all forms of biomimetic design for AM involves complexity in various forms not previously encountered.

biomimetic design

AM

complexity


Input: While AM relaxes the traditional manufacturing rules, not any geometry or structure can be produced easily or reliably.

AM

traditional manufacturing

geometry

structure

be


Input: Due to the complexity of design, design for AM becomes even more crucial to ensure manufacturability and might involve re-design in cases of difficult geometries.

complexity

manufacturability

geometries

design

design

AM


Input: Metal AM and its limits in general are discussed in more detail in.

Metal AM

limits


Input: AM is still a relatively new manufacturing process which requires process optimization and quality control to ensure accuracy and reliability.

AM

manufacturing process

process optimization

quality control

accuracy

reliability


Input: This requirement is critically important for parts with complex geometries which include curved surfaces, thin connecting features, hidden features and lattice structures.

complex geometries

curved surfaces

lattice structures


Input: There are also many varieties of AM with different trade names, processes and differences in quality obtained.

AM

processes

quality


Input: This quality refers in particular to material density and process induced pores, inherent process surface roughness, build errors such as uneven powder spreading or scan track errors leading to critical flaws, residual stresses and associated warping and cracking and microstructural inhomogeneity.

quality

process

process

errors

flaws

warping

cracking

microstructural

material

as

powder

density

pores

roughness

residual stresses

build


Input: A major limitation is the minimum feature size for the AM system used.

minimum feature size

AM


Input: Some additional limitations are placed on the part designs, most notably the build angles.

designs

build


Input: All down-facing surfaces have typically rougher surfaces than upwards-facing surfaces, thin angled features suffer from stair-step effects, and small angles require supports.

surfaces

surfaces

surfaces

supports


Input: Support removal is not a simple process: this post-processing is time consuming and may also affect the dimensional accuracy and quality of the resulting part.

Support

simple

process

post-processing

quality

dimensional accuracy


Input: When supports are needed inside a complex part, these supports might not be physically removable at all as shown in the example in 18.

supports

supports

be

as


Input: In this figure, two topology-optimized bracket designs were almost entirely latticed but the build process required incorporation of supports also inside the lattice region.

bracket

build

supports

lattice


Input: Removing supports from lattice regions on the exterior can cause damage to the lattice struts, and removing them from inside the lattice region is entirely impossible.

supports

lattice

lattice

lattice

damage


Input: In this case, the brackets still met the mass target despite internal supports, but the aesthetic value is not as visually impressive as could have been achieved by appropriate design to eliminate supports.

supports

supports

aesthetic

as

as

design


Input: Detailed inspection of these complex parts ensures their structural integrity and accuracy of production.

inspection

accuracy

structural integrity

production


Input: Due to the expense involved in AM, non-destructive tools are especially useful to analyze parts without destroying them: the most widely used are X-ray techniques such as 2D digital radiography and 3D micro-computed tomography.

AM

tools

X-ray

as

radiography


Input: Due to the complexity of the parts 2D X-ray images are difficult to interpret and smaller flaws which are typical to AM may be missed.

complexity

images

flaws

AM

be


Input: As a result, microCT is often the preferred method of choice.

As

microCT


Input: This technique works by acquisition of X-ray absorption images from many angles around the object, followed by reconstruction to produce a 3D representation of the object, including its interior.

X-ray absorption

images

reconstruction


Input: It is also known as X-ray tomography, CT scanning or X-ray microscopy.

as

CT

X-ray

microscopy


Input: The most important issues that can be identified by microCT and which are relevant to biomimetic AM are:-Powder can get stuck in complex areas, especially inside lattices, and when heat-treated they become stuck.

be

Powder

microCT

biomimetic AM

areas

lattices

heat-treated


Input: This adds weight and might be unsafe.

weight

be


Input: -Rough surfaces which depend on build angle might affect mechanical properties, with rough surfaces in inaccessible areas being unable to be processed.

surfaces

mechanical properties

surfaces

build

areas

be


Input: Roughness can be measured quantitatively or assessed visually.

Roughness

be

quantitatively


Input: -Manufacturing flaws such as porosity might also occur despite process parameter optimization and this may affect the mechanical properties.

Manufacturing

flaws

process parameter

optimization

mechanical properties

as


Input: It is important to note here that process parameter optimization prior to building a part can limit process-induced porosity and this microporosity is expected to be the same in a test coupon than in a complex part.

process parameter

optimization

limit

porosity

microporosity

be


Input: -Residual stress can not directly be seen in microCT images but can be seen indirectly in the form of warping and cracks.

Residual stress

be

be

microCT

images

warping


Input: Unnoticed residual stress in a part might affect its mechanical properties.

residual stress

mechanical properties


Input: Stress-relief heat treatment is therefore highly recommended.

heat treatment


Input: The above issues can be partially improved or solved by using AM simulations to highlight where thermal hotspots might be formed.

be

be

AM


Input: A change in the build angle or design itself can contribute to eliminate these.

build

design


Input: Changing the lattice design or parameters can improve the requirement for supports and self-supporting lattice designs can be selected in some cases.

lattice design

self-supporting

lattice designs

parameters

supports

be


Input: Besides build orientation planning and simulation, the manufacturing process can be optimized to ensure high quality production on test cubes, which can be subjected to detailed analysis by sectioning, or preferably by microCT.

build orientation

simulation

manufacturing process

production

be

be

quality

microCT


Input: When using microCT, however, it is also important to realize that while small porosity is acceptable when well distributed, only major flaws or those with specific location-specific clustering are important, as well as those in critical regions of the part.

microCT

porosity

flaws

as

as


Input: Optimization of processes using test cubes and microCT may assist in identifying the root cause of some types of defects which allows to improve the process.

Optimization

processes

defects

process

microCT


Input: Simulations and experimental work done on lattice structures with artificially induced porosity in individual struts showed that this did not affect the yield strength of the lattice for up to 0.5 mm pores.

Simulations

experimental

lattice

lattice structures

porosity

yield strength

struts

mm


Input: 8 New trends in biomimetic AM This section mentions some current interesting trends in biomimetic design for AM, with new developments expected in the next few years as the techniques are refined and new tools become available.

trends

trends

biomimetic AM

biomimetic design

AM

as

tools


Input: The first worth mentioning is that most topology optimization software at present operates on the topology itself and subsequently certain areas can be selected for latticing, i.e.

topology optimization

software

topology

areas

be


Input: the latticing is not part of the simulation-driven design process.

simulation-driven

design process


Input: This latticing is incorporated into the simulation-driven design process and will find application especially in light-weighting applications.

simulation-driven

design process


Input: The other useful development is the optimization of repeated lattices gradient lattices and variations of strut thickness or unit cell size across a part, and conformal lattices to the surfaces of a part.

optimization

lattices

lattices

variations

unit cell

surfaces

strut thickness

conformal lattices


Input: In other words, the lattice is not simply cut off on the edge of the part but unit cells are stretched to fit the surface topology.

lattice

unit cells

fit

surface topology


Input: An example hereof is shown in 21 where the lattice is conformal to two opposing surfaces and the lattice density varies to allow denser lattice in areas where simulations show higher stress will be experienced.

lattice

surfaces

lattice

lattice density

areas

simulations

stress

be


Input: Recent research approaches for cellular material design have included the development of multi-scale optimization approaches as described by Osanov and Guest and Cadman.

research

optimization

cellular material

as


Input: In this approach, the unit cell domain is discretized into elements which are then themselves optimized using topology optimization methods, similar to discussions in the previous section.

unit cell

domain

elements

topology optimization


Input: A unit cell so designed can then be used to compute effective properties, after which inverse homogenization is used to upscale the cellular geometry to the level of the larger structure.

unit cell

properties

geometry

structure

designed

be

homogenization


Input: These ideas have been recently extended to multi-material cellular structure optimization.

multi-material

cellular structure


Input: Cellular automata methods have also been developed to design materials and microstructures, and machine learning methods are beginning to be applied to materials design.

design

design

microstructures

be

machine

materials


Input: Because of the very complex shapes of the parts having a biomimetic or bionic design, it is often necessary to use support structures for overhanging areas.

complex shapes

biomimetic

design

support structures

areas


Input: This can be a big problem in the post processing of these parts for removing the supports and surface finishing.

be

post processing

supports

surface finishing


Input: On the other hand, internal complexity and small features are limited in this process, since with constant preheating of each layer to a high temperature, the powder is partially sintered and later can not be removed from the manufactured part.

complexity

process

manufactured

preheating

sintered

layer

temperature

powder

be


Input: There are also quite serious limitations on materials for EBM technology.

materials

EBM


Input: Also recently, companies such as EOS and Velo3D have improved their softwares, scanning strategies and process control parameters, which allowed to realize designs with overhangs lower than 15and large inner diameters without supports.

companies

supports

as

scanning strategies

process control

parameters

designs

overhangs


Input: These developments are all very promising for the realization of increasingly complex biomimetic designs with improved structural integrity and surface quality.

biomimetic designs

structural integrity

surface quality


Input: An emerging trend is the development of software packages incorporating the entire workflow for advanced design for AM, including freeform design, topology optimization, latticing and more recently also build simulation and even support generation and slicing for build preparation.

trend

software

workflow

slicing

design

freeform design

topology optimization

support generation

AM

build

build preparation


Input: When all this is combined in one workspace the entire design process is simplified and this allows more frequent and improved biomimetic designs to be realized in practice.

design process

biomimetic designs

be


Input: The development of standards for AM and non-destructive testing in AM is emerging as an important aspect in the qualification of processes and ensuring reliability in AM processes.

standards

processes

AM

AM

AM processes

non-destructive testing

reliability

as


Input: This is especially applicable to biomimetic designs and it holds the most advantage in optimizing process parameters prior to building complex parts-using microCT test methods.

biomimetic designs

process parameters

microCT


Input: Inspecting complex parts is also valuable in critical parts such as for aerospace, and microCT is the best method to do this.

as

aerospace

microCT


Input: It is worth mentioning that besides complex part inspections, which are limited in resolution by field of view, it is becoming standard practice to inspect witness specimens of smaller diameter built alongside complex parts.

inspections

resolution

standard

diameter


Input: This allows for high resolution CT analysis with defects found in these specimens being indicative of problems encountered during the build.

high resolution

build

CT

defects


Input: Something that is becoming increasingly popular for improving part density is the use of hot isostatic pressing, especially for additively-manufactured metal parts for aerospace it is a requirement that all parts are HIPped.

density

hot isostatic pressing

metal

aerospace


Input: The HIP process closes pores and improves the microstructure, but it is important to realize that not all pores are necessarily closed by HIP: it has been shown that pores connected to the surface do not close properly, and is detectable by microCT.

HIP

HIP

pores

pores

pores

microstructure

surface

microCT


Input: The important point is that HIP should not be used as a blind solution its performance especially in thin walled parts should be checked.

HIP

be

as

be

solution

performance


Input: In general, the use of biomimetic AM is growing at a very fast rate, with practical engineering applications emerging almost daily.

biomimetic AM

engineering


Input: This is driven by the maturation of metal powder bed fusion AM, the development of appropriate software tools, and the huge interest from companies in investing in a technology with clear potential to disrupt various industries.

metal powder bed fusion

AM

software

technology

companies

industries


Input: The key to disrupting existing products is in significant advantages in the new design which is possible by AM and biomimicry is key to unlocking this potential.

design

AM

biomimicry


Input: Besides aesthetic appeal, actual light-weight advantage is likely the biggest drawcard in automotive and aerospace industries.

aesthetic

light-weight

automotive

aerospace industries


Input: In other industries the combination of multiple parts into one might be a significant advantage and it is expected that the multi-functionality of designs might be one of the big future growth areas.

industries

be

be

designs

areas


Input: 9 Conclusions It is clear that biomimicry in AM allows complex functional designs and various tools are currently available to easily achieve such designs.

biomimicry

AM

designs

designs

tools


Input: Biomimetic designs are therefore both beautiful and functional.

Biomimetic designs


Input: Despite the high possible complexity, some design for AM rules have emerged which improve the manufacturability and reliability of these types of parts and these should be incorporated into the design process.

complexity

manufacturability

design process

design

AM

reliability

be


Input: It is especially important that process parameters are optimized to ensure structural integrity and ensure high quality manufacturing, as manufacturing errors might affect these parts more than traditional parts this requires an additional safety factor to be built into designs, and inspection is critical.

process parameters

quality

errors

structural integrity

manufacturing

as

be

safety factor

designs

inspection


Input: Post-processing of parts is also a challenge, and the options are limited therefore depending on the application the complexity of the design might need to be constrained to ensure all surfaces are accessible by required post-processing techniques.

Post-processing

complexity

surfaces

post-processing

design

be


Input: One of the most widely used applications of biomimetic design in AM is light-weighting, but many other opportunities exist including parts customized for acoustic, thermal, optical or other applications, especially in combination with surface modification techniques.

biomimetic design

AM

surface modification

optical


Input: Most importantly, all examples in this work clearly demonstrate that biomimetic designs can be trusted and should be used more widely.

biomimetic designs

be

be


Input: Biomimetic designs are crucial for fully unlocking the power of metal AM in particular.

Biomimetic designs

power

metal AM


Input: In conclusion, biomimicry in AM has been shown to be possible in various ways, with the most accessible tools currently being freeform design and simulation-driven design.

biomimicry

AM

be

tools

freeform design

design

simulation-driven


Input: These tools allow complex forms to be created which often resemble natural structures, and the design engineer may incorporate from naturein this design process.

tools

be

design

design process


Input: For example, in simulation-driven design, various outcomes are possible and selection of the design outcome most similar to a biological structure is most likely the best solution.

simulation-driven

design

design

biological structure

solution


Input: The greatest future potential for biomimicry in AM lies in incorporating real biological input in some ways in the design process and here biological materials science is crucial in providing from naturewhich can be incorporated easily.

biomimicry

design process

AM

biological input

biological materials

be


Input: It is not only in the design process where biomimicry can be employed.

design process

biomimicry

be


Input: The entire process of 3D printing may follow biological principles, including sustainability.

process

sustainability

3D printing


Input: Biomimetic design therefore forms part of and drives the bio-industrial revolution which will become known as Industry 5.0.

Biomimetic design

as


Input: Conflict of interest One author is the Senior VP Business Development & Strategy Simulation Driven Design at Altair Engineering Inc, a provider of software for simulation-driven design amongst others.

Simulation

simulation-driven

Design

Altair Engineering

software