Journal ArticleDOI
A review of machine learning for the optimization of production processes
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TLDR
This study covers the majority of relevant literature from 2008 to 2018 dealing with machine learning and optimization approaches for product quality or process improvement in the manufacturing industry and shows that there is hardly any correlation between the used data, the amount ofData, the machine learning algorithms, the used optimizers, and the respective problem from the production.Abstract:
Due to the advances in the digitalization process of the manufacturing industry and the resulting available data, there is tremendous progress and large interest in integrating machine learning and optimization methods on the shop floor in order to improve production processes. Additionally, a shortage of resources leads to increasing acceptance of new approaches, such as machine learning to save energy, time, and resources, and avoid waste. After describing possible occurring data types in the manufacturing world, this study covers the majority of relevant literature from 2008 to 2018 dealing with machine learning and optimization approaches for product quality or process improvement in the manufacturing industry. The review shows that there is hardly any correlation between the used data, the amount of data, the machine learning algorithms, the used optimizers, and the respective problem from the production. The detailed correlations between these criteria and the recent progress made in this area as well as the issues that are still unsolved are discussed in this paper.read more
Citations
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Journal ArticleDOI
Machine learning and data mining in manufacturing
Alican Dogan,Derya Birant +1 more
TL;DR: A comprehensive literature review is presented to provide an overview of how machine learning techniques can be applied to realize manufacturing mechanisms with intelligent actions and points to several significant research questions that are unanswered in the recent literature having the same target.
Journal ArticleDOI
Tackling Faults in the Industry 4.0 Era-A Survey of Machine-Learning Solutions and Key Aspects.
A. Angelopoulos,Emmanouel T. Michailidis,Nikolaos Nomikos,Panagiotis Trakadas,Antonis Hatziefremidis,Stamatis Voliotis,Theodore Zahariadis +6 more
TL;DR: A detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors.
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State-of-the-art active optical techniques for three-dimensional surface metrology: a review [Invited].
TL;DR: This paper reviews recent developments of non-contact three-dimensional (3D) surface metrology using an active structured optical probe and discusses principles of each technology, and its advantageous characteristics as well as limitations.
Journal ArticleDOI
Machine Learning: New Ideas and Tools in Environmental Science and Engineering
Shifa Zhong,Kai Zhang,Majid Bagheri,Joel G. Burken,April Z. Gu,Baikun Li,Xingmao Ma,Babetta L. Marrone,Zhiyong Jason Ren,Joshua Schrier,Wei Shi,Haoyue Tan,Tianbao Wang,Xu Wang,Xu Wang,Bryan M. Wong,Xusheng Xiao,Xiong Yu,Junjie Zhu,Huichun Zhang +19 more
TL;DR: In this article, the authors explore the potential of ML to revolutionize data analysis and modeling in the field of environmental science and engineering (ESE) field, and cover the essential knowledge needed for such applications.
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A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective
TL;DR: In this paper, the authors present a systematic review of AI-workplace outcomes, focusing on the major functions of human resource management and the process framework of antecedents, phenomenon, outcomes at multiple levels of analysis.
References
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Book
Model Predictive Control
TL;DR: This paper recalls a few past achievements in Model Predictive Control, gives an overview of some current developments and suggests a few avenues for future research.