Accurate predictions of keyhole depths using machine learning-aided simulations
<|reference_start|>Accurate predictions of keyhole depths using machine learning-aided simulations: The keyhole phenomenon is widely observed in laser materials processing, including laser welding, remelting, cladding, drilling, and additive manufacturing. Keyhole-induced defects, primarily pores, dramatically affect the performance of final products, impeding the broad use of these laser-based technologies. The formation of these pores is typically associated with the dynamic behavior of the keyhole. So far, the accurate characterization and prediction of keyhole features, particularly keyhole depth, as a function of time has been a challenging task. In situ characterization of keyhole dynamic behavior using a synchrotron X-ray is complicated and expensive. Current simulations are hindered by their poor accuracies in predicting keyhole depths due to the lack of real-time laser absorptance data. Here, we develop a machine learning-aided simulation method that allows us to accurately predict keyhole depth over a wide range of processing parameters. Based on titanium and aluminum alloys, two commonly used engineering materials as examples, we achieve an accuracy with an error margin of 10 %, surpassing those simulated using other existing models (with an error margin in a range of 50-200 %). Our machine learning-aided simulation method is affordable and readily deployable for a large variety of materials, opening new doors to eliminate or reduce defects for a wide range of laser materials processing techniques.<|reference_end|>
@article{zhang2024accurate,
    title={Accurate predictions of keyhole depths using machine learning-aided
  simulations},
    author={Jiahui Zhang, Runbo Jiang, Kangming Li, Pengyu Chen, Xiao Shang,
  Zhiying Liu, Jason Hattrick-Simpers, Brian J. Simonds, Qianglong Wei, Hongze
  Wang, Tao Sun, Anthony D. Rollett, Yu Zou},
    journal={arXiv preprint arXiv:2402.16190},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16190},
    primaryClass={cond-mat.mtrl-sci cs.CE}
}