Online tool condition monitoring for ultrasonic metal welding via sensor fusion and machine learning

Qasim Nazir, Chenhui Shao

Research output: Contribution to journalArticlepeer-review


In ultrasonic metal welding (UMW), tool wear significantly affects the weld quality and tool maintenance constitutes a substantial part of production cost. Thus, tool condition monitoring (TCM) is crucial for UMW. Despite extensive literature focusing on TCM for other manufacturing processes, limited studies are available on TCM for UMW. Existing TCM methods for UMW require offline high-resolution measurement of tool surface profiles, which leads to undesirable production downtime and delayed decision-making. This paper proposes a completely online TCM system for UMW using sensor fusion and machine learning (ML) techniques. A data acquisition (DAQ) system is designed and implemented to obtain in-situ sensing signals during welding processes. A large feature pool is then extracted from the sensing signals. A subset of features are selected and subsequently used by ML-based classification models. A variety of classification models are trained, validated, and tested using experimental data. The best-performing classification models can achieve close to 100% classification accuracy for both training and test datasets. The proposed TCM system not only provides real-time TCM for UMW but also can support optimal decision-making in tool maintenance. The TCM system can be extended to predict remaining useful life (RUL) of tools and integrated with a controller to adjust welding parameters accordingly.

Original languageEnglish (US)
Pages (from-to)806-816
Number of pages11
JournalJournal of Manufacturing Processes
StatePublished - Feb 2021


  • Machine learning
  • Maintenance
  • Quality control
  • Sensor fusion
  • Tool condition monitoring
  • Ultrasonic metal welding

ASJC Scopus subject areas

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


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