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智能激光微/纳米处理:研究和进步

Yu-Xin Liu1, Wei Gong1, Fan-Gao Bu1

  • 1State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, China.

Nanomaterials (Basel, Switzerland)
|October 15, 2025
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人工智能 (AI),特别是机器学习 (ML),通过改进制造建模和异常检测来增强激光微/纳米处理. 这种整合解决了激光制造中的复杂挑战,以获得更好的结果.

关键词:
在现场检测检测.激光微/纳米处理机器学习是机器学习.预测建模预测建模

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科学领域:

  • 材料科学与工程 材料科学与工程
  • 制造业 制造技术 制造技术
  • 人工智能的人工智能

背景情况:

  • 由于复杂的激光物质相互作用,传统的激光制造面临着挑战,导致不可预测的结果和缺陷.
  • 多步激光工艺容易积累微/纳米缺陷,可能导致灾难性故障.
  • 现有的方法难以精确控制激光微/纳米处理结果.

研究的目的:

  • 审查机器学习 (ML) 在激光微/纳米处理中的整合.
  • 探索ML如何应对激光制造中的挑战.
  • 总结这个跨学科领域当前的进展和未来的方向.

主要方法:

  • 数据驱动和物理驱动建模的整合.
  • 实施智能现场监控.
  • 适应性控制技术在激光处理中的应用.

主要成果:

  • 机器学习在制造过程建模和参数优化方面表现出了卓越的性能.
  • 由人工智能驱动的方法可以实时检测异常,减轻过程故障.
  • ML与激光处理的协同作用增强了控制,减少了缺陷.

结论:

  • 机器学习为下一代激光微/纳米处理提供了重要的智能功能.
  • 基于ML的技术有效地克服了激光制造中的传统局限性.
  • 未来的研究有望在激光技术中通过AI驱动的先进应用.