A tutorial on deep learning-based data analytics in manufacturing through a welding case study. (March 2021)

• Record Type: Journal Article
• Authors: Wang, Qiyue; Jiao, Wenhua; Wang, Peng; Zhang, YuMing
• Is Part Of: Journal of manufacturing processes. Volume 63(2021)
• Page Start: 2
• Page End: 13
• Publication Date: 2021-03
• Subjects: Deep learning -- Smart manufacturing -- Quality prediction -- Welding; Production management -- Data processing -- Periodicals; Manufacturing processes -- Periodicals; Procestechnologie; Productietechniek; Production -- Gestion -- Informatique -- Périodiques; Fabrication -- Périodiques; Manufacturing processes; Production management -- Data processing; Periodicals; 670.5
• DOI: 10.1016/j.jmapro.2020.04.044
• Languages: English
• ISSNs: 1526-6125

Abstract

Abstract: Over the past decade, machine learning and deep learning have been increasingly reshaping manufacturing towards smart manufacturing. This paper aims to provide a tutorial for researchers to understand the basic principles of deep learning and its applications to manufacturing, using welding as an example. In this tutorial, we first present an overview of welding processes and the advantages of deep learning in solving welding problems, such as process monitoring and product quality prediction. Then, deep learning characteristics are summarized and two representative deep learning techniques, conventional neural networks (CNNs) and recurrent neural networks (RNNs) that are suitable for image processing and sequential modeling, are discussed. A case study on welding quality prediction that predicts the back-side bead width from top-side images through a CNN is demonstrated, with detailed procedures and core codes from building a CNN to testing the network performance. Prospects for deep learning in a manufacturing context are examined from the authors' perspective.