AI-Driven Qualification for Scalable Laser Powder Bed Fusion

with Yash Parikh, Ph.D.
Process Engineering Consultant, EOS North America

Wednesday, May 6th, 2026
3:30 pm to 4:45 pm EST
Kelly Hall Rm. 310

Laser Powder Bed Fusion (LPBF) adoption for serial production is often hindered by the high cost and time of traditional empirical qualification. Transitioning to efficient, model-based strategies is critical for scaling mission-critical components. This presentation demonstrates a data-driven framework that leverages multi-modal In-Situ Process Monitoring (ISPM) and artificial intelligence to bypass these bottlenecks. By capturing real-time deviations with high-resolution sensors such as Optical Tomography (OT) and Melt Pool Monitoring (MPM), we build predictive models that link in-situ signatures to final part quality. For Ti-6Al-4V, statistical models utilizing OT standard deviations accurately predict Yield Strength (R² = 83%) and Tensile Strength (R² = 75%), reducing reliance on destructive testing by up to 50%.

Yash Parikh is a Process Engineering Consultant at EOS North America with over a decade of expertise in additive manufacturing. He specializes in system qualification and has secured over $5 million in research funding as a principal investigator from agencies including the AFRL, ONR, and the DOE. Previously a Postdoctoral Research Associate at Carnegie Mellon University, his Ph.D. research at Texas A&M University on laser powder bed fusion resulted in multiple publications. A certified Lean Six Sigma Green Belt, Dr. Parikh actively contributes to standards organizations like ASTM and ASME, driving innovation through AI/ML and novel materials.