A tutorial on deep learning-based data analytics in manufacturing through a welding case study

Qiyue Wang, Wenhua Jiao, Peng Wang, Yu Ming Zhang

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


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.

Original languageEnglish
Pages (from-to)2-13
Number of pages12
JournalJournal of Manufacturing Processes
StatePublished - Mar 2021

Bibliographical note

Publisher Copyright:
© 2020 The Society of Manufacturing Engineers


  • Deep learning
  • Quality prediction
  • Smart manufacturing
  • Welding

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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