Implementation of Machine Learning for Enhancing Lean Manufacturing Practices for Metal Additive Manufacturing

被引：0

作者：

Vasileska, Ema ^{[1
]}

Argilovski, Aleksandar ^{[1
]}

Tomov, Mite ^{[1
]}

Jovanoski, Bojan ^{[1
]}

Gecevska, Valentina ^{[1
]}

机构：

[1] Faculty of Mechanical Engineering - Skopje, Ss. Cyril and Methodius University in Skopje, North Macedonia, Skopje,1000, Macedonia

来源：

IEEE Transactions on Engineering Management | 2024年 / 71卷

关键词：

Metal additive manufacturing (AM); particularly laser powder bed fusion (LPBF); has emerged as a promising technology for rapidly producing intricate parts while minimizing material waste. However; the widespread adoption of AM has been hindered by the lack of adequate quality control measures. To address this challenge; a large number of machine learning (ML) applications have been proposed to improve the quality and productivity of AM processes. This study proposes the Lean concept as a guiding framework for classifying ML applications according to the Lean principles they support. Through a comprehensive review of literature studies; the research demonstrates the efficacy of this holistic approach; emphasizing ML's contributions to the Lean principles and the derived benefits to refine metal AM practices; improve efficiency; foster continuous improvement in LPBF; and finally bring value to the customer. The obtained results are particularly important for manufacturing engineers; quality control specialists; and decision-makers in the AM industry; as they provide actionable insights for enhancing process reliability; reducing waste; and achieving higher productivity. © 1988-2012 IEEE;

D O I：

10.1109/TEM.2024.3459645

中图分类号：

学科分类号：

摘要：

引用

页码：14836 / 14845

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