Abstract A production line is a set of sequential operations established in a factory where materials are put through a refining process to produce an end-product that is suitable for further usage. Monitoring production lines is essential to ensure that the targeted quality of the production process and the products are achieved. With the increased digitalization, lots of data can now be generated in the overall production line process. In parallel, the generated data sets are used by machine learning techniques for analytics of the production line to improve quality control, evaluate risks, and save cost. This paper aims to identify, assess, and synthesize the reported studies related to the application of machine learning in production lines, to provide a systematic overview of the current state-of-the-art and, as such, paving the way for further research. To this end, we have performed a Systematic Literature Review (SLR) in which we retrieved 271 papers, of which 39 primary studies were selected for a detailed analysis. This SLR presents and categorizes the production line problems addressed by machine learning, identifies the targeted industrial domains, discusses which machine learning algorithms have been used, and explains the adopted independent and dependent variables of the models. The study highlights the open problems that need to be solved and provides the identified research directions.

中文翻译：

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机器学习在生产线中的应用：系统文献综述

摘要 生产线是在工厂中建立的一组顺序操作，其中材料经过精炼过程以生产适合进一步使用的最终产品。监控生产线对于确保实现生产过程和产品的目标质量至关重要。随着数字化程度的提高，现在可以在整个生产线过程中生成大量数据。同时，机器学习技术使用生成的数据集对生产线进行分析，以改进质量控制、评估风险并节省成本。本文旨在识别、评估和综合与机器学习在生产线中的应用相关的报告研究，以提供对当前最先进技术的系统概述，因此，为进一步研究铺平道路。为此，我们进行了系统文献综述 (SLR)，检索了 271 篇论文，其中选择了 39 项主要研究进行详细分析。该 SLR 对机器学习解决的生产线问题进行了介绍和分类，确定了目标工业领域，讨论了使用了哪些机器学习算法，并解释了模型采用的自变量和因变量。该研究突出了需要解决的开放性问题，并提供了确定的研究方向。该 SLR 对机器学习解决的生产线问题进行了介绍和分类，确定了目标工业领域，讨论了使用了哪些机器学习算法，并解释了模型采用的自变量和因变量。该研究突出了需要解决的开放性问题，并提供了确定的研究方向。该 SLR 对机器学习解决的生产线问题进行了介绍和分类，确定了目标工业领域，讨论了使用了哪些机器学习算法，并解释了模型采用的自变量和因变量。该研究突出了需要解决的开放性问题，并提供了确定的研究方向。