聚类分析
多样性(控制论)
质量(理念)
产品(数学)
制造工程
过程(计算)
生产(经济)
国家(计算机科学)
计算机科学
机器学习
材料科学
工业工程
系统工程
人工智能
工程类
算法
几何学
操作系统
哲学
宏观经济学
经济
认识论
数学
作者
Chengcheng Wang,Xipeng Tan,Shu Beng Tor,C.S. Lim
标识
DOI:10.1016/j.addma.2020.101538
摘要
Additive manufacturing (AM) has emerged as a disruptive digital manufacturing technology. However, its broad adoption in industry is still hindered by high entry barriers of design for additive manufacturing (DfAM), limited materials library, various processing defects, and inconsistent product quality. In recent years, machine learning (ML) has gained increasing attention in AM due to its unprecedented performance in data tasks such as classification, regression and clustering. This article provides a comprehensive review on the state-of-the-art of ML applications in a variety of AM domains. In the DfAM, ML can be leveraged to output new high-performance metamaterials and optimized topological designs. In AM processing, contemporary ML algorithms can help to optimize process parameters, and conduct examination of powder spreading and in-process defect monitoring. On the production of AM, ML is able to assist practitioners in pre-manufacturing planning, and product quality assessment and control. Moreover, there has been an increasing concern about data security in AM as data breaches could occur with the aid of ML techniques. Lastly, it concludes with a section summarizing the main findings from the literature and providing perspectives on some selected interesting applications of ML in research and development of AM.
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