Review
Additive Manufacturing
Additive Manufacturing (AM) has become increasingly recognised as an
important alternative method of manufacture, particularly for highly
customised parts or parts with complex geometries [2]. The drivers
for these first applications are practical issues, such as manufacturing
flexibility, reduction in tooling costs, or better utilisation of
material and sustainability [3]. As AM has become a more mature
capability, more challenging applications are coming into consideration,
such as components for aerospace or space vehicles, and with this,
greater concern for the materials properties of the as-manufactured
component [4]. The drivers for such applications include the
environmental and material cost requirement to reduce the“buy-to-fly” ratio for expensive aerospace alloys, or to
manufacture lightweight components with an internal hollow structure
that are impossible to make any other way. Another emerging and
important driver is the ability to manufacture parts in the field or at
remote locations.
A more significant issue for AM for critical load bearing component
applications is to know the mechanical performance of additively
manufactured material. For the United States Air Force (USAF), the
prediction of the Durability and Damage Tolerance (DADT) of a metallic
part is based on linear-elastic fracture mechanics (LEFM) principles
[5, 6]. This analysis process makes extensive use of what is termed
the “equivalent initial damage size (EIDS)” , which represents
the size of the damage that must be assumed to exist when the aircraft
enters service. The size of the EIDS required for AM aerospace parts is
discussed in [6].
It is well-known that surface finish has an influence on the fatigue
performance of a test specimen, and so it is natural that the surface
roughness of an as-manufactured AM component has received a great deal
of interest from researchers looking to extend AM as a method of
manufacturing aerospace parts [7, 8, 9, 10, 11, 12], for example,
[8] stated:
“The surface roughness is the single most severe factor for fatigue for
additive manufactured materials”.
The purpose of this current paper is to argue that surface roughness is
not the only critical consideration, and to demonstrate that considering
both surface roughness and sub-surface porosity can reveal more detail
about the mechanism and development of damage accumulation in the
material over multiple loading cycles. The published literature contains
some indications of the significance of considering both surface
roughness and sub-surface porosity together, rather than as two separate
phenomena, but in general it does not seem to be generally recognised.
For example, Masuo et al [13] investigated defects, surface
roughness and HIPping of Titanium alloy AM. They describe two types of
pores: gas pores and lack of fusion (LOF). They state, “Many defects
which were formed at subsurface were eliminated by HIP and eventually
HIP improved fatigue strength drastically…”, and note that
surface polishing and HIPping substantially improve fatigue properties.
On closer inspection of their stress-life (S-N) graphs it can be clearly
seen that surface machining alone has a greater improvement than HIPping
alone, but when both operations were performed the improvement was
greater than might be assumed from summing the improvements from the two
individual effects. In another example, Chan and Peralta-Duran [14]
consider fatigue in AM parts, and use an analytical model to treat
surface notches as fatigue crack nucleation sites. Their measured
fatigue life results for as-built AM parts do not seem to follow the
trend lines of their predictions, whereas the equivalent results for
surface machined AM parts do. This seems to suggest that, in neglecting
the combined surface morphology effects of neighbouring notches and
sub-surface porosity, an important physical aspect is missing from their
model.
It is hoped that the present computationally based study can help to
identify the significant physical aspects that must be taken into
account. Ideally, it would be desirable to be able to in link physical
surface morphology feature measurements directly to the EIDS value used
to certify AM parts for aerospace applications.
Fatigue life testing, statistics and approaches for aircraft
certification
However good a model is, the physical experiment is generally preferred
because there may be parameters or effects within the real test specimen
that are not measured or appreciated in the analysis but have a
significant effect on the result obtained. Fatigue life prediction has
always been built on test data and statistical analysis with the test
specimens made from the same material and fabricated using the same
manufacturing processes as the engineering component for which the
fatigue life prediction is required. The practical attractions of
Additive Manufacturing have to be tempered with the crucial requirement
that the predicted operational life must be conservative. Since
certification requires a damage tolerance/durability analysis [4,
5], there have been many test programmes from which the crack growth
rate versus the stress intensity range during a load cycle curves,\(\frac{\text{da}}{\text{dN}}\) versus \(K\), have been generated for a
range of metal alloys, and different Additive Manufacturing processes as
well as for conventionally manufactured aerospace materials [15-22].
A review of the “state of the art” of the damage tolerant and
durability analysis methodologies needed for aerospace applications is
given in [22]. In this context [23-27] have shown that the
Hartman-Schijve variant of the NASGRO crack growth equation can be used
to represent crack growth in AM materials accurately as well as crack
growth in parts repaired using additive metal deposition. The fact that
the same formulation works so well for such a wide range of materials
and manufacturing processes suggests that a phenomenologically based
predictive understanding of fatigue life could be within grasp.
Regardless of which crack growth equation is used in the DTDA
design/assessment of an AM part, or an AM repair to an existing part,
the choice of the EIDS is a key factor in determining the operational
life of the part. Here it is important to note that, as stated in the
certification standard MIL-STD-1530D [5], the role of testing is
merely to “validate or correct analysis methods and results” .
This raises the question can we relate EIDS to a physical quantity?
As previously noted the certification requirements for AM components in
military aircraft are enunciated in the recently published EZ-SB-19-01
[5], which is in-turn based on MIL-STD-1530D [5]. Prior to the
introduction of EZ-SB-19-01, Structures Bulletin EZ-SB-13-001 [28]
stated that AM is “NOT RECOMMENDED without extensive testing and
AFRL/RX [Air Force Research Laboratory, Materials and Manufacturing
Directorate] support” . Thus, while not recommended, the use of
AM was not entirely ruled out. MIL-STD-1530D set out the evaluation
requirements of (i) “Stability” (here this refers to
process stability), (ii) “Producibility” (the need to
reproduce the same capabilities at volume production rates), (iii)
“Characterization of […] properties” ,(iv) “Predictability of structural performance” , and (v)
“Supportability” (product sustainment throughout the lifecycle).
The 2019 EZ-SB-19-01 [5] directive builds on these requirements, and
discusses a range of aspects or features that might contribute or act as“surrogate damage” . To this end EZ-SB-19-01 cites “four
attributes of surrogate damage: damage type, damage size, damage
orientation, and damage location” . Thus, both surface roughness and
defect size are considered together, and (to some extent) in the same
way. It further discusses the damage tolerance approach and the
requirement for an “Equivalent Initial Damage Size (EIDS)” .
Here EIDS is defined as per MIL-STD-1530D [5], viz. :
“an analytical characterization of the initial quality of the aircraft
structure at the time of manufacture, modification or repair. The EIDS
distribution is derived by analytically determining the initial damage
size distribution that characterizes the measured damage size
distribution observed during test or in service.”
Given that the operational life of the structure is determined by
analysis, this means that the EIDS is determined by the size of the
initial flaw that will yield the measured test life. As explained by
Lincoln [29], when the USAF adopted damage tolerance, they made the
decision to separate the process for assessing safety from the process
for assessing aircraft durability. Consequently, as shown in [29],
EIDS can be a function of the \(\frac{\text{da}}{\text{dN}}\) versus\(K\) curve used in the analysis. Furthermore, Lincoln also revealed
that for a durability analysis it is necessary to use the\(\frac{\text{da}}{\text{dN}}\) versus \(K\) curve corresponding to the
growth of small naturally occurring cracks. This is explained in more
detail in [13]. If this is not done then the EIDS values are a
function of the test spectra [29]. On the other hand, if the small
crack \(\frac{\text{da}}{\text{dN}}\) versus \(K\) curve is used in the
DTDA analysis, then the EIDS is closely related to the actual size of
the material discontinuities from which the cracks grow [16-17, 25-27,
29-32].
EZ-SB-19-01 notes that for AM parts surface roughness is a key physical
property; however, surface roughness is strongly dependent on the AM
process, and the choice of definition for roughness [33-36], with
surface roughness sizes that can lie in the range of 10s to 100s of μm.
In this context, the use of the fractal box dimension to characterize
surface roughness is particularly appealing given its success in
characterising crack growth [37-41], its ability to characterize the
failure surfaces associated with additive metal deposition [40], and
its role in the development of the Boeing Bogel surface
treatment [42].
With regard to flaws such as defects, inclusions, porosity pores and
surface breaking features, these are also typically in the size range
10s to 100s of μm. Finfrock et al. [43] describe the occurrence of
porosity for parts made using Selective Laser Melting (SLM),
highlighting the value of the HIPping process and the quality of the
feedstock powder. Figure 1 illustrates the situation of porosity
occurring close to the surface: the pore illustrated is roughly 50 μm
across and centred at about 100 μm below the nominal surface. In
another study by Du Plessis et al [44], using X-ray micro CT to
examine AM Titanium alloy material subjected to HIPping, clusters of
< 70 μm pores at a sub-surface depth of about 300 μm are
illustrated. The authors explain, by analogy to similar observations
made of cast components, that sub-surface pores that are connected to
the surface by micro-cracks cannot be eliminated by HIPping.
Tammas-Williams et al [45] and Léonard et al [46]
also use X-ray CT to investigate defect location and type in Titanium
alloy samples made using the SLM. Tammas-Williams et al state
that the “majority of pores” are “spherical and relatively small
(< 75 μm)” and that “only ~ 3% of pores”
have an aspect ratio of greater than 1.5. In another interesting paper
by Guo et al [47] laser shock peening of AM Titanium alloy is
investigated. The paper illustrates a sub-surface pore of nearly
circular form, with a diameter of approximately 5 μm, and situated about
30 μm beneath the surface. For further examples, Kruth et al [48]
review a huge variety of AM process.
Finally, it should be noted that EZ-SB-19-01 [6] requires a minimal
EIDS of 0.01 inches (0.254 mm), and that Airbus have stated that, for AM
parts, an EIDS of greater than 0.5 mm is rarely seen [49]. This
latter statement is important given the statement in EZ-SB-19-01 that
for the damage size for durability crack growth analysis shall be based
on a probability of exceeding the EIDS of 1x10-3.
Representative modelling
approaches
There are two main areas of work on which this present work is based.
Firstly, there is the body of work concerned with computational
modelling of heterogeneous materials, in order to model their
properties. The second area is concerned with the modelling of surface
texture. In both cases, it is assumed that there would be a full
computational analysis of the modelled geometry, probably using Finite
Element Analysis (FEA), and probably including some non-linear elastic
or elastoplastic material properties, to determine stresses and plastic
strains. An interesting proxy approach is proposed [50] whereby the
strain field can be related to a geodesic property, which might provide
a faster but more approximate method for assessing such models.
Modelling of heterogeneous materials
The work in this area is very wide ranging, and includes heterogeneity
in many forms, from the random patterns of metal crystal grain
structure, porosity and modelling of foams, through to the regular
structure of a perfect composite material. Authors typically model a
Representative Volume Element (RVE) of material, applying boundary
conditions based on symmetry conditions [50-55]. Where the objective
is to understand bulk material properties based on a detailed local
model, this is a sensible approach: consider a notional pair of
neighbouring RVEs. Both would be undergoing similar levels of loading
and deforming in a similar way. The boundary between them would transfer
little overall stress or strain when averaged over its length.