Texas A&M University — Industrial and Systems Engineering
College Station, TX · US · publishing in AM since 2013
Data-driven process control and machine learning for metal AM
Alaa Elwany is Professor of Industrial and Systems Engineering at Texas A&M University, where his group brings statistical learning, Bayesian process modelling, and Gaussian-process surrogate methods to LPBF parameter qualification. His research focuses on uncertainty quantification in process–structure–property relationships, optimal design of experiments for LPBF parameter envelopes, and real-time anomaly detection from co-axial melt-pool and thermal imaging data. Elwany's group has contributed to the development of physics-informed machine learning models for predicting melt-pool geometry, porosity, and microstructure, with case studies in Ti-6Al-4V, Inconel 718, and 17-4 PH stainless steel. He has been an NSF CAREER awardee and serves on the editorial boards of the Journal of Manufacturing Science and Engineering and Additive Manufacturing.
Last reviewed: 2026-05-16 · Sources: 2
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