MechProNet: Machine Learning Prediction of Mechanical Properties in Metal Additive Manufacturing

arXiv:2209.12605v2 Announce Type: replace
Abstract: Predicting mechanical properties in metal additive manufacturing (MAM) is essential for ensuring the performance and reliability of printed parts, as well as their suitability for specific applications. However, conducting experiments to estimate mechanical properties in MAM processes can be laborious and expensive, and they are often limited to specific materials and processes. Machine learning (ML) methods offer a more flexible and cost-effective approach to predicting mechanical properties based on processing parameters and material properties. In …

abstract additive manufacturing applications arxiv cond-mat.mtrl-sci cs.ai cs.lg however machine machine learning manufacturing metal performance prediction processes reliability type