Preprint,
Data-driven Parameters Tuning for Predictive Performance Improvement of Wire Bonder Multi-body Model
Affiliations
- [1] Wageningen University & Research [NORA names: Netherlands; Europe, EU; OECD];
- [2] Eindhoven University of Technology [NORA names: Netherlands; Europe, EU; OECD];
- [3] University of Groningen [NORA names: Netherlands; Europe, EU; OECD];
- [4] University of Southern Denmark [NORA names: SDU University of Southern Denmark; University; Denmark; Europe, EU; Nordic; OECD];
- [5] University of Oxford [NORA names: United Kingdom; Europe, Non-EU; OECD];
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Abstract
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.