Expert Systems with Applications ( IF 8.5 ) Pub Date : 2021-03-04 , DOI: 10.1016/j.eswa.2021.114820 Massimo Bertolini , Davide Mezzogori , Mattia Neroni , Francesco Zammori
Machine Learning (ML) is a branch of artificial intelligence that studies algorithms able to learn autonomously, directly from the input data. Over the last decade, ML techniques have made a huge leap forward, as demonstrated by Deep Learning (DL) algorithms implemented by autonomous driving cars, or by electronic strategy games. Hence, researchers have started to consider ML also for applications within the industrial field, and many works indicate ML as one the main enablers to evolve a traditional manufacturing system up to the Industry 4.0 level. Nonetheless, industrial applications are still few and limited to a small cluster of international companies. This paper deals with these topics, intending to clarify the real potentialities, as well as potential flaws, of ML algorithms applied to operation management. A comprehensive review is presented and organized in a way that should facilitate the orientation of practitioners in this field. To this aim, papers from 2000 to date are categorized in terms of the applied algorithm and application domain, and a keyword analysis is also performed, to details the most promising topics in the field. What emerges is a consistent upward trend in the number of publications, with a spike of interest for unsupervised and especially deep learning techniques, which recorded a very high number of publications in the last five years. Concerning trends, along with consolidated research areas, recent topics that are growing in popularity were also discovered. Among these, the main ones are production planning and control and defect analysis, thus suggesting that in the years to come ML will become pervasive in many fields of operation management.
中文翻译:
面向工业应用的机器学习:全面的文献综述
机器学习(ML)是人工智能的一个分支,研究能够直接从输入数据自主学习的算法。在过去的十年中,机器学习技术取得了巨大的飞跃,自动驾驶汽车或电子策略游戏实现的深度学习(DL)算法证明了这一点。因此,研究人员开始考虑将ML也应用于工业领域,许多研究表明ML是将传统制造系统发展到工业4.0级的主要推动力之一。尽管如此,工业应用仍然很少,并且仅限于一小群国际公司。本文讨论了这些主题,旨在阐明应用于操作管理的ML算法的实际潜力以及潜在缺陷。提出并组织了全面的审查,其方式应有助于该领域从业者的定位。为此,根据应用算法和应用领域对2000年至今的论文进行分类,并进行关键字分析,以详细说明该领域最有希望的主题。出现的趋势是出版物数量呈持续上升趋势,对无监督学习技术(尤其是深度学习技术)的兴趣激增,在过去五年中记录了非常多的出版物。关于趋势以及合并的研究领域,还发现了越来越流行的最新主题。其中主要是生产计划和控制以及缺陷分析,