Laser material processing optimization using bayesian optimization: a generic tool Article Swipe

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Tobias Menold , Volkher Onuseit , Matthias Buser , Michael J. Haas , Nico Bär , Andreas Michalowski ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.37188/lam.2024.032 · OA: W4399348236

Optimizing laser processes is historically challenging, requiring extensive and costly experimentation. To solve this issue, we apply Bayesian optimization for process parameter optimization to laser cutting, welding, and polishing. We demonstrate how readily available Bayesian optimization frameworks enable efficient optimization of laser processes with only modest expert knowledge. Case studies on laser cutting, welding, and polishing highlight its adaptability to real-world manufacturing scenarios. Moreover, the examples emphasize that with suitable cost functions and boundaries an acceptable optimization result can be achieved after a reasonable number of experiments.