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

IF 4.6 3区 管理学 Q1 BUSINESS
Ema Vasileska;Aleksandar Argilovski;Mite Tomov;Bojan Jovanoski;Valentina Gecevska
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引用次数: 0

Abstract

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.
实施机器学习,加强金属增材制造的精益生产实践
金属增材制造(AM),尤其是激光粉末床熔融技术(LPBF),已成为一种既能快速生产复杂零件,又能最大限度减少材料浪费的前景广阔的技术。然而,由于缺乏适当的质量控制措施,AM 的广泛应用受到了阻碍。为了应对这一挑战,人们提出了大量机器学习(ML)应用,以提高 AM 流程的质量和生产率。本研究提出了精益概念,作为根据精益原则对 ML 应用进行分类的指导框架。通过对文献研究的全面回顾,研究证明了这一整体方法的有效性,强调了 ML 对精益原则的贡献,以及在完善金属 AM 实践、提高效率、促进 LPBF 的持续改进并最终为客户带来价值方面的益处。所获得的结果对制造工程师、质量控制专家和 AM 行业的决策者尤为重要,因为它们为提高工艺可靠性、减少浪费和实现更高的生产率提供了可行的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Engineering Management
IEEE Transactions on Engineering Management 管理科学-工程:工业
CiteScore
10.30
自引率
19.00%
发文量
604
审稿时长
5.3 months
期刊介绍: Management of technical functions such as research, development, and engineering in industry, government, university, and other settings. Emphasis is on studies carried on within an organization to help in decision making or policy formation for RD&E.
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