{"citation":{"chicago":"Hesse, Michael, Matthias Hunstig, Julia Timmermann, and Ansgar Trächtler. “Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-Forward Control Design.” In <i>Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)</i>, 383–94, 2022.","ama":"Hesse M, Hunstig M, Timmermann J, Trächtler A. Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design. In: <i>Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)</i>. ; 2022:383-394.","ieee":"M. Hesse, M. Hunstig, J. Timmermann, and A. Trächtler, “Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design,” in <i>Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)</i>, Online, 2022, pp. 383–394.","mla":"Hesse, Michael, et al. “Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-Forward Control Design.” <i>Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)</i>, 2022, pp. 383–94.","short":"M. Hesse, M. Hunstig, J. Timmermann, A. Trächtler, in: Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM), 2022, pp. 383–394.","apa":"Hesse, M., Hunstig, M., Timmermann, J., &#38; Trächtler, A. (2022). Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design. <i>Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)</i>, 383–394.","bibtex":"@inproceedings{Hesse_Hunstig_Timmermann_Trächtler_2022, title={Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design}, booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)}, author={Hesse, Michael and Hunstig, Matthias and Timmermann, Julia and Trächtler, Ansgar}, year={2022}, pages={383–394} }"},"quality_controlled":"1","page":"383-394","publication":"Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)","_id":"29803","user_id":"29222","year":"2022","author":[{"full_name":"Hesse, Michael","id":"29222","last_name":"Hesse","first_name":"Michael"},{"last_name":"Hunstig","full_name":"Hunstig, Matthias","first_name":"Matthias"},{"first_name":"Julia","last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia"},{"first_name":"Ansgar","full_name":"Trächtler, Ansgar","id":"552","last_name":"Trächtler"}],"abstract":[{"text":"Ultrasonic wire bonding is a solid-state joining process used to form electrical interconnections in micro and\r\npower electronics and batteries. A high frequency oscillation causes a metallurgical bond deformation in\r\nthe contact area. Due to the numerous physical influencing factors, it is very difficult to accurately capture\r\nthis process in a model. Therefore, our goal is to determine a suitable feed-forward control strategy for the\r\nbonding process even without detailed model knowledge. We propose the use of batch constrained Bayesian\r\noptimization for the control design. Hence, Bayesian optimization is precisely adapted to the application of\r\nbonding: the constraint is used to check one quality feature of the process and the use of batches leads to\r\nmore efficient experiments. Our approach is suitable to determine a feed-forward control for the bonding\r\nprocess that provides very high quality bonds without using a physical model. We also show that the quality\r\nof the Bayesian optimization based control outperforms random search as well as manual search by a user.\r\nUsing a simple prior knowledge model derived from data further improves the quality of the connection.\r\nThe Bayesian optimization approach offers the possibility to perform a sensitivity analysis of the control\r\nparameters, which allows to evaluate the influence of each control parameter on the bond quality. In summary,\r\nBayesian optimization applied to the bonding process provides an excellent opportunity to develop a feedforward\r\ncontrol without full modeling of the underlying physical processes.","lang":"eng"}],"keyword":["Bayesian optimization","Wire bonding","Feed-forward control","model-free design"],"publication_identifier":{"isbn":["978-989-758-549-4"]},"title":"Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design","conference":{"name":"11th International Conference on Pattern Recognition Applications and Methods","start_date":"2022-02-03","end_date":"2022-02-05","location":"Online"},"language":[{"iso":"eng"}],"date_created":"2022-02-09T12:50:25Z","department":[{"_id":"153"}],"status":"public","type":"conference","date_updated":"2023-11-06T15:17:12Z"}