About this event
Density plays a key role in additive manufacturing (AM) parameter development. However, in a recent study, we uncovered how even for samples above 99% density, mechanical properties can vary significantly, with our samples showing almost 20% variation for ultimate tensile strength and more than 45% for yield stress.
Due to time and cost restraints, standard processes for parameter development tend to rely heavily on density in the first stages, with mechanical properties only being brought in once the down-selection of parameters have been completed. However, with the initial data shortages, surprisingly misleading results, and unexpected material behaviour uncovered in our study, this approach exposes users to costly delays and stifled innovation potential.
So, what does this mean for “best practice” in AM parameter development? Join AMS CEO, former Airbus engineer, and all-round AM expert, Rob Higham, and materials scientist and Plastometrex CTO, Dr Jimmy Campbell, at 2pm BST on the 21st of March as they discuss the challenges of parameter development, how the findings from their recent study could present an opportunity for users, and whether a change in approach could work in practice.
This webinar will cover:
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Robert is the founder, CEO & director of AMS. He launched Additive Manufacturing Solutions Ltd. (AMS) in 2017. Robert is a Chartered Engineer and has experience across academia, motorsport, space and aerospace. Robert was responsible for qualification of materials, processes and parts produced by additive manufacturing most recently for Airbus before creating AMS. Robert is also a PhD candidate where he is investigating multi laser effects on the metallurgy of AM products.
As Chief Technology Officer I have developed numerical methods for rapid extraction of mechanical properties from indentation data. These are deployed in our method to extract stress-strain curves from an indentation test, which is called Profilometry based Indentation Plastometry (PIP).
Plastometrex is combining materials science, advanced numerical modelling, optimisation methods, and machine learning tools to create new generation materials testing solutions.