Case Study: Boeing accelerates additive manufacturing parameter optimisation with Intellegens Alchemite™

Accelerating AM process parameter optimisation with machine learning

Boeing, working with the University of Sheffield AMRC, needed a faster, less costly way to optimise additive manufacturing (AM) process parameters for metallic aerospace alloys, where small changes in powder, machine setup, or environment can strongly affect results. Intellegens’ Alchemite™ deep learning software was used to help guide laser powder bed fusion (LPBF) parameter development using sparse experimental data.

Intellegens applied Alchemite™ in two project phases to learn from historic builds, predict outcomes for new builds, and optimise parameters for Constellium’s Aheadd® CP1 powder on GE Additive’s 400W M2 machine. The approach delivered close agreement between predicted and experimental results, and the team moved from a new powder to final parameters in just two builds, with no expert statistical knowledge required.

Boeing

Lukas Jiranek

Boeing