Deep learning for smart manufacturing: Methods and applications

• Published in: Journal of manufacturing systems Vol. 48; pp. 144 - 156
• Main Authors: Wang, Jinjiang, Ma, Yulin, Zhang, Laibin, Gao, Robert X., Wu, Dazhong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2018
• Subjects: Computational intelligence; Data analytics; Deep learning; Smart manufacturing; Deep learning; Data analytics; Computational intelligence; Smart manufacturing
• ISSN: 0278-6125; 1878-6642
• DOI: 10.1016/j.jmsy.2018.01.003

•Evolvement of deep learning technologies and their advantages over traditional machine learning are discussed.•Computational methods based on deep learning are presented to improve system performance.•Emerging topics and future trends of deep learning for smart manufacturing are summarized. Smart manufacturing refers to using advanced data analytics to complement physical science for improving system performance and decision making. With the widespread deployment of sensors and Internet of Things, there is an increasing need of handling big manufacturing data characterized by high volume, high velocity, and high variety. Deep learning provides advanced analytics tools for processing and analysing big manufacturing data. This paper presents a comprehensive survey of commonly used deep learning algorithms and discusses their applications toward making manufacturing “smart”. The evolvement of deep learning technologies and their advantages over traditional machine learning are firstly discussed. Subsequently, computational methods based on deep learning are presented specially aim to improve system performance in manufacturing. Several representative deep learning models are comparably discussed. Finally, emerging topics of research on deep learning are highlighted, and future trends and challenges associated with deep learning for smart manufacturing are summarized.