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机器学习用于功能分级材料的增材制造

Mohammad Karimzadeh¹, Deekshith Basvoju², Aleksandar Vakanski²

¹Department of Computer Science, University of Idaho, Moscow, ID 83844, USA.

Materials (Basel, Switzerland)

|August 10, 2024

概括

本综述探讨了机器学习 (ML) 如何优化功能级材料 (FGM) 的增材制造 (AM). ML解决了FGM制造方面的挑战,提高了各行各业的组件性能.

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:

增材制造 (AM) 可从3D数据直接制造复杂的零件.
功能分级材料 (FGM) 通过在材料之间进行过渡来提供量身定制的性能.
在各种行业中,FGM具有显著的潜力来提高组件性能.

研究的目的:

在AM中全面审查有关机器学习 (ML) 实施的文献.
专注于基于ML的方法来优化FGM制造过程.
探索ML在解决FGM生产中的挑战中的作用.

主要方法:

对已发表的关于女性生殖器切割在女性生殖器切割中ML的文献进行了广泛的调查.
对FGM的参数优化中ML应用的分析.
在AM中检测缺陷和实时监控的ML技术的审查.

主要成果:

ML技术越来越多地应用于克服FGM制造中固有的挑战.
机器学习有助于优化过程参数,改善缺陷检测,并使实时监控成为可能.
机器学习的整合增强了AM生产先进FGM的潜力.

结论:

机器学习提供了强大的工具,可以通过增材制造来推进功能级材料的制造.
需要进一步的研究来应对挑战,并释放基于ML的FGM的AM的全部潜力.
基于ML的优化对于实现FGM在工业应用中的好处至关重要.

关键词:

添加剂制造添加剂制造添加剂制造定向能量沉积是指向能量沉积的方法.具有功能分级的材料.机器学习是机器学习.

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