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http://hdl.handle.net/1942/36279
Title: | Multi-objective simulation optimization of the adhesive bonding process of materials | Authors: | MORALES HERNANDEZ, Alejandro VAN NIEUWENHUYSE, Inneke ROJAS GONZALEZ, Sebastian JORDENS, Jeroen Witters, Maarten Van Doninck, Bart |
Corporate Authors: | Jeroen Jordens Maarten Witters Bart Van Doninck |
Issue Date: | 2021 | Source: | Winter Simulation Conference, Phoenix, Arizona, United States, 13/12/2021-17/12/2021 | Status: | In press | Abstract: | Automotive companies are increasingly looking for ways to make their products lighter, using novel materials and novel bonding processes to join these materials together. Finding the optimal process parameters for such adhesive bonding process is challenging. In this research, we successfully applied Bayesian optimization using Gaussian Process Regression and Logistic Regression, to efficiently (i.e., requiring few experiments) guide the design of experiments to the Pareto-optimal process parameter settings. | Keywords: | multi-objective;machine learning;process optimization;bayesian optimization | Document URI: | http://hdl.handle.net/1942/36279 | Category: | C2 | Type: | Conference Material |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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MOSK_WSC2021 corrected authors.pdf | Conference material | 83.12 kB | Adobe PDF | View/Open |
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