Data-driven Parameters Tuning for Predictive Performance Improvement of Wire Bonder Multi-body Model
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.
Authors 10
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Wageningen University & Research
Affiliation as printed
Wageningen University & Research
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Eindhoven University of Technology
Affiliation as printed
Eindhoven University of Technology
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Affiliation as printed
University of Groningen
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Affiliation as printed
University of Groningen
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University of Southern Denmark
Affiliation as printed
University of Southern Denmark
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Affiliation as printed
University of Oxford
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Delft University of Technology
Affiliation as printed
Delft University of Technology
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Qiyao Peng Aachen
Affiliation as printed
Leiden University
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Technische Universität Braunschweig
Affiliation as printed
Technische Universität Braunschweig
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Affiliation as printed
Vrije Universiteit Amsterdam
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