Constructing a heat source parameter estimation model for heat conduction finite element analysis using deep convolutional neural network

Heat conduction finite element analysis (FEA) is an important technique for estimating the temperature history of processes with local heat input, such as welding and additive manufacturing. This study proposes a framework to find appropriate heat source parameters without depending on the analyst's skill. The heat source parameter estimation model consists of pre-trained deep convolutional and fully connected neural networks. The model determines appropriate heat source parameters such as the heat input, base metal shapes and temperature history. The model was constructed using a database created by heat conduction FEA. We demonstrated that heat source parameters were determined accurately for both known and unknown conditions.

Automated summary

This study proposes a framework based on deep learning networks to accurately estimate the temperature history of processes with local heat input and find appropriate heat source parameters. The results of the study demonstrate the accurate determination of heat source parameters under known and unknown conditions.