Data-driven Parameters Tuning for Predictive Performance Improvement of Wire Bonder Multi-body Model

This report describes work performed during SWI 2023 at the University of Groningen in relation with Problem 1 posed by the company ASMPT. They have detailed simulation software of a machine and they compare the results of this with physical experimental results. There is a significant difference between the simulated and measured data, and it is the goal of this work to study how to estimate the parameters in the simulation model using the ex-perimentally measured frequency response. First, two toy models are studied to understand the challenges of pa- rameter estimation in the frequency domain. Later, optimization methods are applied. Several different approaches of reducing the dimensionality of the parameter space are explored, including determining the parameter sensitivity. A suggestion for increasing the detail of the model, specifically related to the machine base, is also outlined. In the summary, we supply a discussion of the key insights we gained.