Case Study with AMRC and Boeing
“Alchemite™ was able to converge on the optimum solution with far fewer experiments.”
Ian Brooks, Technical Fellow, AMRC on the benefits of applying machine learning to cut experimental time and costs in additive manufacturing process development.
- Machine learning exploits existing data to guide AM projects on the most productive pathway.
- Alchemite™ can significantly reduce the number of experiments required to achieve a result, saving time and cost.
Video: Ian Brooks from AMRC speaking at an Intellegens webinar (View full recording).
Machine learning technology is being used to make the additive manufacturing (AM) process of metallic alloys for aerospace cheaper and faster, encouraging the production of lightweight, energy-efficient aircraft to support net-zero targets for aviation.
Project MEDAL is led by Intellegens, the University of Sheffield AMRC North West, and global aerospace giant Boeing. It is using machine learning to optimise AM processing parameters for new metal alloys at a lower cost and faster rate.
James Hughes, Research Director for University of Sheffield AMRC North West explained: “At the AMRC we have experienced first-hand, and through our partner network, how onerous it is to develop a robust set of process parameters for AM. It relies on a multi-disciplinary team of engineers and scientists and comes at great expense in both time and capital equipment. It is our intention to develop a robust, end-to-end methodology for process parameter development that encompasses how we operate our machinery right through to how we generate response variables quickly and efficiently. Intellegens’ AI-embedded platform Alchemite™ will be at the heart of all of this.”
Ian Brooks, Technical Fellow at AMRC, provided an update on progress in an Intellegens webinar, showing how Alchemite™ machine learning was being applied to achieve a significant reduction in the number of experiments required to converge on a solution.