Ultrasonic metal welding (USMW) is a solid-state joining process used in various industries such as the automotive or electronics industries for its energy efficiency and other advantages. Despite its widespread use, it currently suffers from unexplained fluctuations in the weld strength, and there is a need for a robust and reliable real-time online monitoring process. Such a process has been proved challenging to develop due to the unclear relation between measurable machine processes, such as the energy consumption or welding time, and the weld strength. For this reason, most monitoring approaches in the literature have relied on black-box, data-driven approaches which do not use knowledge about the welding process. However, due to their nature, these approaches may have limited interpretability and generalizability to other welding conditions. These limitations could be addressed with a monitoring approach based on knowledge of the physics of the welding process. These insights relate the evolution of the weld strength during welding to the vibrations of the horn and anvil, two key components of the welding machine, at the welding frequency. These insights further divide USMW into six distinct stages, each involving distinct thermomechanical processes, with the fifth stage having the maximum weld strength.

The aim of this thesis is to develop a knowledge-based monitoring approach based on these six stages. The first goal is to develop a real-time monitoring approach based on the horn and anvil vibrations at the welding frequency, tracking the progress of the welding process through its welding stages. The second goal is to investigate how well features extracted using knowledge of the welding stages can estimate the weld strength. The third goal of this thesis is to investigate the usability of other monitoring parameters, namely the horn and anvil vibrations at harmonics of the welding frequency and the airborne sound radiated from the machine during welding, to monitor USMW. The fourth goal of this thesis is to improve the overall understanding of the horn and anvil vibrations at the welding frequency by training an equivalent mechanical model to track the welding stages. Due to their knowledge-based nature, the results of these goals promise to be suitable or easily adaptable to a large number of welding conditions.

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