Machine Learning and Data Mining in Manufacturing. Dogan, A. & Birant, D. Expert Systems with Applications, September, 2020. ![Machine Learning and Data Mining in Manufacturing [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Manufacturing organizations need to use different kinds of techniques and tools in order to fulfill their foundation goals. In this aspect, using machine learning (ML) and data mining (DM) techniques and tools could be very helpful for dealing with challenges in manufacturing. Therefore, in this paper, 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. Furthermore, it points to several significant research questions that are unanswered in the recent literature having the same target. Our survey aims to provide researchers with a solid understanding of the main approaches and algorithms used to improve manufacturing processes over the past two decades. It presents the previous ML studies and recent advances in manufacturing by grouping them under four main subjects: scheduling, monitoring, quality, and failure. It comprehensively discusses existing solutions in manufacturing according to various aspects, including tasks (i.e., clustering, classification, regression), algorithms (i.e., support vector machine, neural network), learning types (i.e., ensemble learning, deep learning), and performance metrics (i.e., accuracy, mean absolute error). Furthermore, the main steps of knowledge discovery in databases (KDD) process to be followed in manufacturing applications are explained in detail. In addition, some statistics about the current state are also given from different perspectives. Besides, it explains the advantages of using machine learning techniques in manufacturing, expresses the ways to overcome certain challenges, and offers some possible further research directions.
@article{dogan_machine_2020,
title = {Machine {Learning} and {Data} {Mining} in {Manufacturing}},
issn = {0957-4174},
url = {http://www.sciencedirect.com/science/article/pii/S095741742030823X},
doi = {10.1016/j.eswa.2020.114060},
abstract = {Manufacturing organizations need to use different kinds of techniques and tools in order to fulfill their foundation goals. In this aspect, using machine learning (ML) and data mining (DM) techniques and tools could be very helpful for dealing with challenges in manufacturing. Therefore, in this paper, 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. Furthermore, it points to several significant research questions that are unanswered in the recent literature having the same target. Our survey aims to provide researchers with a solid understanding of the main approaches and algorithms used to improve manufacturing processes over the past two decades. It presents the previous ML studies and recent advances in manufacturing by grouping them under four main subjects: scheduling, monitoring, quality, and failure. It comprehensively discusses existing solutions in manufacturing according to various aspects, including tasks (i.e., clustering, classification, regression), algorithms (i.e., support vector machine, neural network), learning types (i.e., ensemble learning, deep learning), and performance metrics (i.e., accuracy, mean absolute error). Furthermore, the main steps of knowledge discovery in databases (KDD) process to be followed in manufacturing applications are explained in detail. In addition, some statistics about the current state are also given from different perspectives. Besides, it explains the advantages of using machine learning techniques in manufacturing, expresses the ways to overcome certain challenges, and offers some possible further research directions.},
language = {en},
urldate = {2020-10-05},
journal = {Expert Systems with Applications},
author = {Dogan, Alican and Birant, Derya},
month = sep,
year = {2020},
keywords = {Machine learning, classification, clustering, data mining, manufacturing},
pages = {114060},
}
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Furthermore, it points to several significant research questions that are unanswered in the recent literature having the same target. Our survey aims to provide researchers with a solid understanding of the main approaches and algorithms used to improve manufacturing processes over the past two decades. It presents the previous ML studies and recent advances in manufacturing by grouping them under four main subjects: scheduling, monitoring, quality, and failure. It comprehensively discusses existing solutions in manufacturing according to various aspects, including tasks (i.e., clustering, classification, regression), algorithms (i.e., support vector machine, neural network), learning types (i.e., ensemble learning, deep learning), and performance metrics (i.e., accuracy, mean absolute error). Furthermore, the main steps of knowledge discovery in databases (KDD) process to be followed in manufacturing applications are explained in detail. 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