Experimental, computational, and machine learning methods for prediction of residual stresses in laser additive manufacturing: A critical review

SH Wu, U Tariq, R Joy, T Sparks, A Flood, F Liou - Materials, 2024 - mdpi.com
In recent decades, laser additive manufacturing has seen rapid development and has been
applied to various fields, including the aerospace, automotive, and biomedical industries …

[HTML][HTML] Weight Factor as a Parameter for Optimal Part Orientation in the L-PBF Printing Process Using Numerical Simulation

Ľ Kaščák, J Varga, J Bidulská, R Bidulský, D Manfredi - Materials, 2024 - mdpi.com
The L-PBF process belongs to the most modern methods of manufacturing complex-shaped
parts. It is used especially in the automotive, aviation industries, and in the consumer …

Thermal-Mechanical Physics Informed Deep Learning For Fast Prediction of Thermal Stress Evolution in Laser Metal Deposition

R Sharma, YB Guo - arxiv preprint arxiv:2412.18786, 2024 - arxiv.org
Understanding thermal stress evolution in metal additive manufacturing (AM) is crucial for
producing high-quality components. Recent advancements in machine learning (ML) have …