Karimzadeh Mohammad, Basvoju Deekshith, Vakanski Aleksandar, Charit Indrajit, Xu Fei, Zhang Xinchang
Department of Computer Science, University of Idaho, Moscow, ID 83844, USA.
Department of Nuclear Engineering and Industrial Management, University of Idaho, Idaho Falls, ID 83402, USA.
Materials (Basel). 2024 Jul 25;17(15):3673. doi: 10.3390/ma17153673.
Additive Manufacturing (AM) is a transformative manufacturing technology enabling direct fabrication of complex parts layer-by-layer from 3D modeling data. Among AM applications, the fabrication of Functionally Graded Materials (FGMs) has significant importance due to the potential to enhance component performance across several industries. FGMs are manufactured with a gradient composition transition between dissimilar materials, enabling the design of new materials with location-dependent mechanical and physical properties. This study presents a comprehensive review of published literature pertaining to the implementation of Machine Learning (ML) techniques in AM, with an emphasis on ML-based methods for optimizing FGMs fabrication processes. Through an extensive survey of the literature, this review article explores the role of ML in addressing the inherent challenges in FGMs fabrication and encompasses parameter optimization, defect detection, and real-time monitoring. The article also provides a discussion of future research directions and challenges in employing ML-based methods in the AM fabrication of FGMs.
增材制造（AM）是一种变革性的制造技术，能够根据三维建模数据逐层直接制造复杂零件。在增材制造应用中，功能梯度材料（FGM）的制造具有重要意义，因为它有可能提高多个行业的部件性能。功能梯度材料是由不同材料之间的梯度成分过渡制造而成，能够设计出具有位置相关机械和物理性能的新材料。本研究全面综述了已发表的有关机器学习（ML）技术在增材制造中的应用的文献，重点是基于机器学习的优化功能梯度材料制造工艺的方法。通过对文献的广泛调研，这篇综述文章探讨了机器学习在解决功能梯度材料制造中固有挑战方面的作用，包括参数优化、缺陷检测和实时监测。文章还讨论了在功能梯度材料的增材制造中采用基于机器学习的方法的未来研究方向和挑战。
