{"title":"增材制造中的机器学习:全面洞察","authors":"Md Asif Equbal , Azhar Equbal , Zahid A. Khan , Irfan Anjum Badruddin","doi":"10.1016/j.ijlmm.2024.10.002","DOIUrl":null,"url":null,"abstract":"<div><div>Additive manufacturing (AM) is a technological advancement gaining colossal popularity due to its advantages and simplified fabrication. AM facilitates the manufacturing of complex, light, and strong products from digitized designs. With recent advancements, AM can bring digital flexibility and improved efficiency to industrial operations. Despite the various advantages, there is continuous variation in the qualities of AM products, which remains the main challenge in the wide application of AM. The performance of printed parts is directly influenced by processing parameters, and adjusting the parameters in the AM process can be quite challenging. The barrier can be minimized by proper monitoring of the AM process and precise measurement of AM materials and components, which is difficult to achieve through analytical and numerical models. Current research demonstrates machine learning (ML) and its techniques as a novel way to reduce costs. It also helps achieve optimal process design and part quality using the fundamentals of the AM process. ML is a subcategory of artificial intelligence (AI) that enables systems to learn and improve from measured data and past experiences. The present paper is focused on presenting a broad understanding of the current applications of ML in AM and thus provides a solid background for practitioners and researchers to apply ML in AM. Very few earlier reviews were presented before, but their studies mostly focus on artificial neural network technology and other irrelevant papers. In addition, most papers were published in 2021 and 2022 and were not discussed in earlier reviews. This state-of-the-art review is based on the latest database collected from Web of Science (WoS), Publons, Scopus, and Google Scholar using machine learning and additive manufacturing as the keywords. Extensive information collected on the possible applications of ML in AM shows that ML can be effectively applied to improve AM part quality and process reliability.</div></div>","PeriodicalId":52306,"journal":{"name":"International Journal of Lightweight Materials and Manufacture","volume":"8 2","pages":"Pages 264-284"},"PeriodicalIF":0.0000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning in additive manufacturing: A comprehensive insight\",\"authors\":\"Md Asif Equbal , Azhar Equbal , Zahid A. 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Current research demonstrates machine learning (ML) and its techniques as a novel way to reduce costs. It also helps achieve optimal process design and part quality using the fundamentals of the AM process. ML is a subcategory of artificial intelligence (AI) that enables systems to learn and improve from measured data and past experiences. The present paper is focused on presenting a broad understanding of the current applications of ML in AM and thus provides a solid background for practitioners and researchers to apply ML in AM. Very few earlier reviews were presented before, but their studies mostly focus on artificial neural network technology and other irrelevant papers. In addition, most papers were published in 2021 and 2022 and were not discussed in earlier reviews. This state-of-the-art review is based on the latest database collected from Web of Science (WoS), Publons, Scopus, and Google Scholar using machine learning and additive manufacturing as the keywords. 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引用次数: 0
摘要
增材制造(AM)是一项技术进步,由于其优势和简化的制造而获得巨大的普及。增材制造有助于从数字化设计中制造复杂、轻便和坚固的产品。随着最近的进步,增材制造可以为工业运营带来数字灵活性和更高的效率。尽管有各种各样的优势,但增材制造产品的质量不断变化,这仍然是增材制造广泛应用的主要挑战。打印件的性能直接受到加工参数的影响,在增材制造过程中调整参数是相当具有挑战性的。通过对增材制造过程的适当监控和对增材制造材料和部件的精确测量,可以最大限度地减少这种障碍,这是通过分析和数值模型难以实现的。目前的研究表明,机器学习(ML)及其技术是降低成本的一种新方法。它还有助于利用增材制造工艺的基本原理实现最佳工艺设计和零件质量。机器学习是人工智能(AI)的一个子类,它使系统能够从测量数据和过去的经验中学习和改进。本文的重点是对当前机器学习在增材制造中的应用进行广泛的理解,从而为从业者和研究人员在增材制造中应用机器学习提供坚实的背景。之前很少有早期的综述,但他们的研究主要集中在人工神经网络技术和其他不相关的论文。此外,大多数论文发表于2021年和2022年,未在早期综述中讨论。这篇最先进的综述基于从Web of Science (WoS)、Publons、Scopus和b谷歌Scholar收集的最新数据库,以机器学习和增材制造为关键词。收集了大量关于机器学习在增材制造中可能应用的信息,表明机器学习可以有效地应用于提高增材制造零件质量和工艺可靠性。
Machine learning in additive manufacturing: A comprehensive insight
Additive manufacturing (AM) is a technological advancement gaining colossal popularity due to its advantages and simplified fabrication. AM facilitates the manufacturing of complex, light, and strong products from digitized designs. With recent advancements, AM can bring digital flexibility and improved efficiency to industrial operations. Despite the various advantages, there is continuous variation in the qualities of AM products, which remains the main challenge in the wide application of AM. The performance of printed parts is directly influenced by processing parameters, and adjusting the parameters in the AM process can be quite challenging. The barrier can be minimized by proper monitoring of the AM process and precise measurement of AM materials and components, which is difficult to achieve through analytical and numerical models. Current research demonstrates machine learning (ML) and its techniques as a novel way to reduce costs. It also helps achieve optimal process design and part quality using the fundamentals of the AM process. ML is a subcategory of artificial intelligence (AI) that enables systems to learn and improve from measured data and past experiences. The present paper is focused on presenting a broad understanding of the current applications of ML in AM and thus provides a solid background for practitioners and researchers to apply ML in AM. Very few earlier reviews were presented before, but their studies mostly focus on artificial neural network technology and other irrelevant papers. In addition, most papers were published in 2021 and 2022 and were not discussed in earlier reviews. This state-of-the-art review is based on the latest database collected from Web of Science (WoS), Publons, Scopus, and Google Scholar using machine learning and additive manufacturing as the keywords. Extensive information collected on the possible applications of ML in AM shows that ML can be effectively applied to improve AM part quality and process reliability.