Hybrid full-field thermal characterization of additive manufacturing processes using physics-informed neural networks with data. (arXiv:2206.07756v1 [cs.LG])

Understanding the thermal behavior of additive manufacturing (AM) processes
is crucial for enhancing the quality control and enabling customized process
design. Most purely physics-based computational models suffer from intensive
computational costs, thus not suitable for online control and iterative design
application. Data-driven models taking advantage of the latest developed
computational tools can serve as a more efficient surrogate, but they are
usually trained over a large amount of simulation data and often fail to
effectively use small but high-quality experimental …

additive manufacturing arxiv data hybrid lg manufacturing networks neural networks physics processes