Abstract: Distortion is a common problem in welded structures, and the process of finding an effective weld sequence to mitigate the distortion is a challenging task given a large number of possible combinations. Despite efficient simulation tools and powerful supercomputers, yet simulation tools have been limited by CPU time to optimize a welding sequence and therefore not mature for practical designs. To this end, we constructed and integrated machine learning (ML) algorithms with the simulation capability. These ML models were then trained to increase the fidelity by a wisely chosen training-set of simulation to construct a meta-model for active exploration of various weld sequence scenarios in real time. As opposed to existing ML algorithms that require an extensive data set to train, our algorithm picks relatively small training-set to construct a metamodel. We present an example of our algorithm implemented in a real welded structure project.
Authors: Mahyar Asadi, Mohammad Mohseni, Majid Tanbakuei Kashani, Michael Fernandez, and Mathew Smith
Keywords: Weld Distortion Control, Weld Sequence Design, Machine Learning for Welding, Digital Weld Engineering, Weld Modeling and Simulation, Data-Driven Modeling