Abstract
The demand for industrial development toward advanced and precision manufacturing has sparked interest in ultrafast laser-based micromachining methods, particularly emerging advanced machining methods, such as laser-induced plasma micromachining (LIPMM). The main challenge in laser micromachining is finding the optimal process in a large process space to achieve a comprehensive improvement in processing efficiency and quality as approaches that rely on trial-and-error are impractical. In this work, we combined data-driven machine learning and physical model into a cycle design strategy, in order to efficient capture the comprehensive optimization process of LIPMM with high material removal rate and high microgroove depth. Based on the small sample dataset and additional physical variables provided by the physical model, the optimal process in the whole process space can be obtained using only four design cycles and dozens of data groups, and the material removal rate and microgroove depth of which are improved comprehensively compared with the original data. The design strategy integrated with physical model presented here could be applied in a wide range of fields, and thus shows the promise of accelerating the development of laser micromachining processes.
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The data that support the findings of this study are available from the corresponding author upon request.
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Acknowledgements
This work is financially supported by National Key R&D Program (Grant No. 2021YFB3702502). This research is supported by the Industry-University-Institute Cooperation Program of Aero Engine Corporation of China, China (Grant No. HFZL2020CXY014-1) and National Science and Technology Major Project (J2019-VII-0013-0153).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by ZZ, CW and ZY. The first draft of the manuscript was written by ZZ and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Zhang, Z., Yang, Z., Wang, C. et al. Accelerating ultrashort pulse laser micromachining process comprehensive optimization using a machine learning cycle design strategy integrated with a physical model. J Intell Manuf 35, 449–465 (2024). https://doi.org/10.1007/s10845-022-02058-0
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DOI: https://doi.org/10.1007/s10845-022-02058-0