Hybrid Process Prognosis for Metal Ultrasonic Welding - Pro²MUSS

• Applicants: Professor Dr.-Ing. Burkhard Corves; Professor Dr.-Ing. Uwe Reisgen
• Subject Area: Joining and Separation Technology ; Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics ; Measurement Systems
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 520475171

In the project "Hybrid Process Prognosis for Metal Ultrasonic Welding" - Pro²MUSS, the Institute of Welding and Joining Technology at RWTH Aachen University (ISF) and the Institute of Mechanism Theory, Machine Dynamics and Robotics at RWTH Aachen University (IGMR) are jointly developing a new parameterized model for joint formation for a better process understanding in ultrasonic metal welding.Ultrasonic metal welding is particularly suitable for joining electro technical components and is increasingly coming into industrial focus due to the increasing complexity of electronic systems. Despite its widespread use in industry, process fluctuations can occur in metal-ultrasonic welding. These fluctuations often cannot be explained, since there is a lack of scientifically sound knowledge about the complex interactions between tools and joining parts during the welding process and, as a result, they are hardly taken into account in the mostly empirical research. Within the framework of joint research projects (AiF IGF project 20.161) and basic investigations by the ISF (DFG project number 395129909 / GZ RE 2755/52-1), it has already been shown that information on the thermomechanical processes occurring within the joining zone can be obtained from the externally measurable vibration behaviour of the overall mechanical system, consisting of welding tools and joining parts. The vibration behaviour of the overall system correlates with the joint formation.The two project partners use this basis to create a parameterized model of joint formation. For this purpose, the project partners create two submodels. With a statistical-deterministic modelling of the process course, essential key figures of the process, such as welding duration, performance course and in particular the characteristics of the process phases are to be determined on the basis of process boundary conditions such as geometry, welding parameters and material properties. The aim of this sub-model is thus to predict a typical course of the process on the basis of the parameters mentioned. This model can be understood as a macroscopic view of the process. A second, machine learning (ML) based modelling is intended to provide a statement on the actually achieved joint quality of the individual weld based on measurements of process parameters. The modelling represents the microscopic joint quality. By linking both models, individual mechanisms of joint formation and process phases are to be assigned to components of the ML and, as far as possible, rewritten in a parameterized form. The overall model depicts the welding process from the parameters through the process sequence to the achievable or actually achieved joint quality.

• DFG Programme: Research Grants