An end-to-end online quality prediction system for ultrasonic metal welding based on deep learning
Wu, Yulun
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https://hdl.handle.net/2142/112943
Description
Title
An end-to-end online quality prediction system for ultrasonic metal welding based on deep learning
Author(s)
Wu, Yulun
Issue Date
2021-05-19
Director of Research (if dissertation) or Advisor (if thesis)
Shao, Chenhui
Department of Study
Mechanical Sci & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
ultrasonic metal welding
quality prediction
welding signal
signal fusion
deep learning
Abstract
Ultrasonic metal welding (UMW) is an important joining technology that is widely used in industry. In many UMW applications, there is a strong need for predicting joint quality quickly, reliably, and non-destructively. State-of-the-art quality assessment methods such as destructive tensile testing and quality monitoring cannot meet the high requirements in industrial-scale production. This thesis proposes a novel end-to-end online prediction algorithm for UMW based on deep learning that offers various benefits, including superior quality prediction, less reliance on prior knowledge of UMW processes (e.g., tool conditions), and not involving tedious data preprocessing and feature engineering. The effectiveness of the proposed method is shown using real-world data generated from a UMW process. A comparative case study is presented to compare three data fusion strategies (early fusion, middle fusion, and late fusion) and traditional feature engineering-based methods. The results show that the proposed end-to-end quality prediction system outperforms traditional methods. In addition, the middle fusion strategy achieves the best prediction performance.
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