Machine learning (ML) (a subset of artificial intelligence that focuses on autonomous computer knowledge gain) is actively being used across many domains, such as entertainment, commerce, and increasingly in industrial settings. The wide applicability and low barriers for development of these algorithms are allowing for innovations, once thought unattainable, to be realized in an ever more digital world. As these innovations continue across industries, the manufacturing industry has also begun to gain benefits. With the current push for Smart Manufacturing and Industrie 4.0, ML for manufacturing is experiencing unprecedented levels of interest; but how much is industry actually using these highly-publicized techniques? This paper sorts through a decade of manufacturing publications to quantify the amount of effort being put towards advancing ML in manufacturing. This work identifies both prominent areas of ML use, and popular algorithms. This also allows us to highlight any gaps, or areas where ML could play a vital role. To maximize the search space utilization of this investigation, ML based Natural Language Processing (NLP) techniques were employed to rapidly sort through a vast corpus of engineering documents to identify key areas of research and application, as well as uncover documents most pertinent to this survey. The salient outcome of this research is the presentation of current focus areas and gaps in ML applications to the manufacturing industry, with particular emphasis on cross domain knowledge utilization. A full detailing of methods and findings is presented.

中文翻译：

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机器学习在智能制造中的应用与发展综述

机器学习 (ML)（人工智能的一个子集，专注于自主计算机知识获取）正在许多领域得到积极应用，例如娱乐、商业，并且越来越多地应用于工业环境。这些算法的广泛适用性和低开发障碍使得曾经被认为无法实现的创新在日益数字化的世界中得以实现。随着这些创新在各个行业不断进行，制造业也开始受益。随着当前智能制造和工业 4.0 的推动，制造业机器学习正受到前所未有的关注；但工业界实际使用了多少这些广为人知的技术呢？本文对十年来的制造业出版物进行了整理，以量化在制造业中推进机器学习所付出的努力。这项工作确定了 ML 使用的突出领域和流行算法。这也使我们能够突出机器学习可以发挥重要作用的任何差距或领域。为了最大限度地利用本次调查的搜索空间，采用基于机器学习的自然语言处理 (NLP) 技术对大量工程文档进行快速分类，以确定研究和应用的关键领域，并发现与本次调查最相关的文档。这项研究的显着成果是介绍了制造业 ML 应用的当前重点领域和差距，特别强调跨领域知识利用。详细介绍了方法和发现。