Matthew W. Priddy
Papers
1
Total Citations
2
H-Index
1
About
Dr. Matthew W. Priddy is a leading researcher in advanced manufacturing and computational mechanics, with a primary focus on wire arc-directed energy deposition (WA-DED) and physics-informed machine learning. His most notable contribution is the development of Thermal Physics-Informed PointNet (TPI-PointNet), a groundbreaking framework that integrates thermal physics constraints with deep learning to predict distortion in additive manufacturing processes. This work, published in 2025 and already garnering 2 citations, addresses a critical challenge in WA-DED—the geometric deformation caused by uneven thermal expansion and contraction during layer-wise production. By fusing physical principles with data-driven models, Dr. Priddy’s approach enables more accurate, real-time defect prediction, advancing the reliability of metal additive manufacturing for industrial applications. His research sits at the intersection of thermal science, structural mechanics, and artificial intelligence, offering practical solutions for process optimization and quality control. Dr. Priddy’s work is particularly impactful for researchers and engineers seeking to mitigate distortion-induced failures in large-scale, customized metal components, positioning him as a key innovator in the field of physics-guided machine learning for manufacturing.
Research Focus
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Top Papers
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