CASE STUDY

Beyond Density: Achieving Optimal
AM Parameters with Early Mechanical
Property Testing

In collaboration withIntro
Cost and time constraints have shaped
the conventional workflow in Additive

Manufacturing (AM) parameter development,
separating parameter down-selection

from mechanical property assessment. Yet,this method, aimed at discovering optimal

parameters, is inherently flawed. It exposes

projects to expensive delays and squanderedinnovation opportunities due to initial data
shortages, potentially misleading results,

and unexpected material behaviour. Could
prioritising mechanical properties from theoutset offer a solution, and is such an approach

conventional parameter development is done
via a 2-stage process envelope:
Phase 1: Density Screening
Development starts with a screening process inwhich density is used as the only down-selection
criteria. Typically, 20-50 cubes are produced,

each with a different set of parameters. In orderto keep printing and material costs low, thesecubes are deliberately small (1 cm3). Parameter

sets that lead to the highest densities areselected, optimised, and taken to phase 2.

feasible in practice?

Phase 2: Mechanical Property Assessment

Metal additive manufacturing machines offer

Once a parameter set is identified, the focus

numerous production parameters that influence
the melting and solidification of each printed

alloy. For laser powder bed fusion (LPBF) theseinclude laser power, scanning speed, layer
thickness, and hatch distance.

To ensure material and part performance,

thorough optimisation of these parameters iscrucial. However, as each of these parameters
can be adjusted independently, effective

parameter selection presents a complex,

shifts to mechanical property assessment

through uniaxial tensile testing in phase 2. Large
qualification builds, incorporating multiple

tensile specimens, are produced to gauge

whether the density optimised parameters

yield satisfactory mechanical performance. It is

typically good practice to collect 30 data points

per condition for statistical significance and highpart confidence. Examples of these qualificationbuilds are shown in figure 1 below.

multi-variable challenge. To break it down,

Figure 1: Example qualification builds for (a) an XACT metal 200C and (b) a Renishaw 500Q
2 ] Beyond Density: Achieving Optimal AM Parameters with Early Mechanical Property TestingChallenge
Parameter sets determine the thermal history

that metals experience during production. Thisnot only influences porosity, it also impacts

the microstructure and resultant mechanicalproperties of the material. Despite this, current

parameter development workflows de-couple
the measurement of mechanical properties(phase 2) from the early parameter down-sets that demonstrate ideal density andselection process (phase 1).

material property combinations are notbeing uncovered, optimised, and utilised in

The reason for this is simple; time and cost. Aparts.

typical tensile coupon has a 30 times higher

material volume than a 1cm3 density cube. This

extra volume drives up testing time, materialand production costs, making the measurementof tensile properties (for a wide range ofparameters) both time and cost prohibitive.

2.

The material properties at the start ofqualification testing (Phase 2) are unknown.
This creates a severe risk – as it is possiblethat the material exhibits acceptabledensity but inadequate material properties

This constraint creates two important issues:

for a given application. In this scenario,

1.

optimised for another qualification run. AA lack of mechanical property data in theearly phase of parameter development
means important trends between

parameters and material properties arebeing missed. Therefore, optimal parameternew parameters must be screened andsingle qualification exercise can cost inexcess of £10,000 and take several weeks ofengineering time, making a re-do exercise asignificant resource drain.

3 ] Beyond Density: Achieving Optimal AM Parameters with Early Mechanical Property TestingObjectives

In this case study, in collaboration withAdditive Manufacturing Solutions, we aimed todetermine if PIP (Profilometry-based Indentation
Plastometry) testing would enable users tomeasure stress-strain curves from 1 cm3 density

cubes. This would empower engineers andscientists to select optimal printing parametersfrom a data set that includes both porosity as
well the fundamental mechanical properties(yield stress and ultimate tensile strength) of thematerial. Importantly, in cases where different
parameter sets lead to similar density values,

PIP testing would enable users to differentiate
samples based on mechanical data.

4 ] Beyond Density: Achieving Optimal AM Parameters with Early Mechanical Property TestingMaterials

Optical density measurements were carried out

on images that were taken using a Nikon Eclipse
Ci-POL camera, initially by using ILASTIK thatapplies a machine learning pixel classification
method, and ImageJ , to measure the relative
area of the pores.

The mechanical properties were measured

using the PLX-Benchtop (figure 2), a compact
indentation-based benchtop device for PIP

testing. PIP uses an accelerated inverse finite

element method to infer accurate stress-strain
curves from indentation test data.

A standard PIP test uses a spherical indenter of2 mm diameter and indents to a depth in theregion of 200 microns. This scale allows a PIP

Figure 2: Plastometrex PLX-Benchtop.

test to be carried out directly on a 1 cm3 density

cube. Only a P1200 micron grind (P600 in NorthAmerican grade) is required for PIP testing andthe test duration is in the region of 5 minutes per
sample, including sample preparation. In thisstudy, 16 parameter cubes were mounted in cold
mount resin into a single sample array block(figure 3). This array could then be prepared ona grinding wheel as a single specimen, reducing
the total preparation time to just 10 minutes.

Figure 3: Sample array featuring 16 parameter cubes..

Berg, S., Kutra, D., Kroeger, T. et al. ilastik: interactive machine learning for (bio)image analysis. Nat Methods 16, 1226–1232 (2019).

1

https://doi.org/10.1038/s41592-019-0582-9
Schneider, C., Rasband, W. & Eliceiri, K. NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9, 671–675 (2012).

2

https://doi.org/10.1038/nmeth.20895 ] Beyond Density: Achieving Optimal AM Parameters with Early Mechanical Property TestingMeasurements

AlSi10Mg was chosen as the example alloy for thiswork as it is a very common printing material,

thereby requiring its parameterisation on manydifferent machines. The 16 samples studied were
produced on an SLM solutions SLM 500 machine.
Surfaces for indentation and microscopy

were prepared to a 1 µm finish. The printing
parameters for the cubes studied were:1.

Laser power (W): 400, 475, 550, 625.

2. Scan speed (mm s-1): 1650 and 2000.
3. Layer thickness (μm): 30 and 60.

6 ] Beyond Density: Achieving Optimal AM Parameters with Early Mechanical Property TestingResults

The printing parameters adopted in this work

were chosen to cover a wide range of potential
parameters. Firstly, the layer thickness wasvaried from 30 μm to 60 μm. This is an importantparameter to optimise, as doubling the layer

thicknesses equates to approximately halving
the build time, which dramatically increases
productivity.

parameter sets leading to similarly high densities
would make it difficult to confidently establish

Secondly, the parameters contributing to energy
density were varied. The energy density window
is important as it showcases the factors which

provide the sweet spot where the metal is fully
melting but not over melting. Energy densities
between 19 and 64 J mm-3 were used in thisstudy.

Initial work assessed the density of the cubes
produced. All cubes showed similar highdensities with 14 of the 16 combinations having adensity above 99%, as shown in figure 4. Several

which had optimal properties without mechanicaldata. However, if tensile testing was being usedto establish their mechanical properties, then asubstantial number of tensile testing coupons (foreach parameter set) would need to be printed

at significant cost and time. Here, the 14 density
cubes with porosity below 1% are already in asuitable condition for PIP testing, which requires
just a flat parallel surface and minimal surface
preparation. (In fact, the surface preparation

requirements are less onerous than those required
for the optical measurements of porosity.)

Figure 4: Plot of measured optical density as a function of energy density
for the 16 different parameter combinations that were explored.

7 ] Beyond Density: Achieving Optimal AM Parameters with Early Mechanical Property Testing

PIP testing was carried out on the samples inthis work to assess their mechanical properties– namely yield and tensile strength, although fullstress-strain curves emerge from the tests. A plotof yield strength and ultimate tensile strength(UTS) against energy density is shown in figure
5, for the 14 samples with a measured density
above 99%.

Even for samples above 99% density, the ultimate
tensile strength variation is almost 20% while theyield stress variation is greater than 45%

Finally, with all of the samples in figure 5 achieving

This plot demonstrates that the energy density

densities of over 99%, this demonstrates thatproperties (yield and tensile strength). Changing

infer similarities in mechanical properties. The

parameters and layer thickness result in different

microstructural features such as phase fractions

case, which changes the microstructure andfor samples above 99% density, the ultimate tensile

properties.

stress variation is greater than 45%.

has a significant effect on the mechanicalsimilar porosity values should never be used tothe processing conditions by varying the laser

mechanical properties will depend on othermelting and solidifications conditions for eachand grain size, not just the porosity fraction. Even

ultimately results in different mechanicalstrength variation is almost 20% while the yield

For each layer thickness studied, both the yieldstrength and ultimate tensile strength decreaseas the energy density increases. As the energy
density increases, larger melt pools will beproduced (and likely more re-melted layers) sosolidification time increases creating a coarser
microstructure which negatively impacts themechanical properties.

The plot also illustrates that even small

differences in yield and tensile strength caneasily be resolved between the samples. In

addition, approximately equivalent mechanicalproperties (yield and tensile strength) can beobtained at both low and high energy density, by

adjusting the layer thickness. With this knowledge
operators have the necessary information to

now optimise the build for both time and costsaving, by utilising the larger layer thickness to

reduce build time while still achieving optimum

Figure 5: Plot of yield strength and UTS as afunction of energy density for the 14 different
parameter combinations that were explored.

mechanical properties.

8 ] Beyond Density: Achieving Optimal AM Parameters with Early Mechanical Property TestingOutcomes

PIP testing has successfully unlocked the ability

the printing time reduced from over 46 hours to

the 1cm3 density cubes used in parameter

typically otherwise is not being performed.

to measure stress-strains curves directly fromdevelopment. This enables users to down-selectparameters based on the optimal combinationof strength and density early in the development
process, an exercise that was previously costprohibitive. This work also shows that different
parameter sets that lead to similar density

values can exhibit very different mechanicalproperties. This suggests that density should not
be used as the only down-selection criteria incases where material strength is important.
Without PIP testing this assessment would

require dozens of tensile testing coupons to beprinted in large builds, costing tens of thousands

9 hours. This cost reduction allows testing that
Incorporating PIP testing into the parameter
development process affords a distinct
competitive advantage, enabling

manufacturers to rapidly identify and leverage
optimal material properties. By advancing

this more informed and efficient approach

to parameter development, companies canexpect to achieve a marked enhancement intheir operational efficiency and product quality,
solidifying their competitive edge in the rapidly
evolving AM sector.

Find out more about PIP Testing for AMof pounds, whereas in these experiments thecost of testing has been reduced by ~95%, and9 ] Beyond Density: Achieving Optimal AM Parameters with Early Mechanical Property TestingSee thetechnology
in action...
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