Unsupervised Multi-Modal Machine Learning for In-Process Monitoring of Additive Manufacturing
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Garland, Anthony Paul↗
Engineering topics
Adamczyk, Jesse
Publications and source records attributed to Adamczyk, Jesse.
Unsupervised Multi-Modal Machine Learning for In-Process Monitoring of Additive Manufacturing
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Garland, Anthony Paul↗
Developing a Practical In-situ AM process Monitoring Framework to Reduce Qualification Burdens
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Heiden, Michael Jacob↗
Functionally graded magnetic materials: a perspective to advance charged particle optics through compositional engineering
Additive manufacturing has ushered in a new paradigm of bottom-up materials-by-design of spatially non-uniform materials. Functionally graded materials have locally tailored compositions to provide optimized global properties and performance. In this letter, we propose an opportunity for the application of graded magnetic materials as lens elements for charged particle optics. A Hiperco50/Hymu80 (FeCo-2 V/Fe-80Ni-5Mo) graded magnetic alloy was successfully additively manufactured via Laser Directed Energy Deposition with spatially varying magnetic properties. The compositional gradient is then applied using computational simulations to demonstrate how a tailored material can enhance the magnetic performance of a critical, image-forming component of a transmission electron microscope.
36 MATERIALS SCIENCE↗
Empowering Additive Manufacturing Qualification through In-Situ Process Monitoring
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Adamczyk, Jesse↗