Data-driven modeling of process, structure and property in additive manufacturing: A review and future directions
Data-driven modeling of process, structure and property in additive manufacturing: A review and future directions
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DOI：
10.1016/j.jmapro.2022.02.053
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发表时间：
2022-05
影响因子：
6.2
通讯作者：
Zhuo Wang;Wenhua Yang;Qingyang Liu;Ying-ying Zhao;Pengwei Liu;Dazhong Wu;M. Banu;Lei Chen
中科院分区：
文献类型：
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作者：
Zhuo Wang;Wenhua Yang;Qingyang Liu;Ying-ying Zhao;Pengwei Liu;Dazhong Wu;M. Banu;Lei Chen
长期以来，人们一直在追求对增材制造（AM）中复杂的工艺-结构-性能（P-S-P）关系的深入了解，因为它对实现AM工艺优化和质量控制至关重要。基于物理的建模和实验方法通常耗时和/或昂贵。随着数字AM数据的日益可用性和数据驱动建模技术的快速发展，特别是机器学习（ML），数据驱动AM建模正在成为实现这一目标的有效方法。它允许自动发现AM数据中的模式和趋势，在参数空间上构建P-S-P关系的定量模型，并在看不见的点进行预测，而无需执行新的物理建模或实验。近年来，基于数据驱动的过程、结构和性能建模技术在敏捷制造领域得到了广泛的研究。在这种情况下，本文的目的是提供一个系统的审查现有的数据驱动的增材制造建模方面的不同数量的利益（QoI）沿着的过程-结构-属性链。具体而言，本文提供了重要信息的摘要（即，输入功能、与QoI相关的输出、数据源和数据驱动模型），以及对迄今为止取得的相关成功的深入分析。在全面综述的基础上，本文还批判性地讨论了当今面临的主要限制，并确定了一些有希望在未来显着推进数据驱动的AM建模的研究方向。
A thorough understanding of complex process-structure-property (P-S-P) relationships in additive manufacturing (AM) has long been pursued due to its paramount importance in achieving AM process optimization and quality control. Physics-based modeling and experimental approaches are usually time-consuming and/or costly. With the increasing availability of digital AM data and rapid development of data-driven modeling techniques, especially machine learning (ML), data-driven AM modeling is emerging as an effective approach towards this end. It allows for automatic discovery of patterns and trends in the AM data, construction of quantitative models of P-S-P relationships over the parameter space and prediction at unseen points without having to perform new physical modeling or experiments. A proliferation of researches on data-driven modeling of process, structure and property in AM have been witnessed in recent years. In this context, this paper aims to provide a systematic review of existing data-driven AM modeling with respect to different quantities of interest (QoI) along the process-structure-property chain. Specifically, this paper provides a summary of important information (i.e., input features, QoI-related output, data source and data-driven models) on existing data-driven AM modeling, as well as an in-depth analysis on relevant success achieved so far. Based on the comprehensive review, this paper also critically discusses the major limitations faced today and identifies some research directions that are promising for significantly advancing data-driven AM modeling in the future.