[{"department":[{"_id":"153"},{"_id":"880"}],"user_id":"82875","_id":"29803","language":[{"iso":"eng"}],"keyword":["Bayesian optimization","Wire bonding","Feed-forward control","model-free design"],"publication":"Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)","type":"conference","status":"public","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"}],"author":[{"id":"29222","full_name":"Hesse, Michael","last_name":"Hesse","first_name":"Michael"},{"first_name":"Matthias","last_name":"Hunstig","full_name":"Hunstig, Matthias"},{"first_name":"Julia","last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia"},{"full_name":"Trächtler, Ansgar","id":"552","last_name":"Trächtler","first_name":"Ansgar"}],"date_created":"2022-02-09T12:50:25Z","date_updated":"2024-11-13T08:44:17Z","conference":{"start_date":"2022-02-03","name":"11th International Conference on Pattern Recognition Applications and Methods","location":"Online","end_date":"2022-02-05"},"title":"Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design","publication_identifier":{"isbn":["978-989-758-549-4"]},"quality_controlled":"1","page":"383-394","citation":{"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.","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} }","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.","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.","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.","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."},"year":"2022"},{"doi":"10.1109/tpami.2021.3051276","title":"AutoML for Multi-Label Classification: Overview and Empirical Evaluation","author":[{"orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","full_name":"Wever, Marcel Dominik","id":"33176","first_name":"Marcel Dominik"},{"full_name":"Tornede, Alexander","id":"38209","last_name":"Tornede","first_name":"Alexander"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier"}],"date_created":"2021-01-16T14:48:13Z","date_updated":"2022-01-06T06:54:42Z","citation":{"chicago":"Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 2021, 1–1. <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">https://doi.org/10.1109/tpami.2021.3051276</a>.","ieee":"M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, pp. 1–1, 2021, doi: <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>.","ama":"Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification: Overview and Empirical Evaluation. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Published online 2021:1-1. doi:<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>","mla":"Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 2021, pp. 1–1, doi:<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>.","bibtex":"@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label Classification: Overview and Empirical Evaluation}, DOI={<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}, year={2021}, pages={1–1} }","short":"M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) 1–1.","apa":"Wever, M. D., Tornede, A., Mohr, F., &#38; Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 1–1. <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">https://doi.org/10.1109/tpami.2021.3051276</a>"},"page":"1-1","year":"2021","publication_status":"published","publication_identifier":{"issn":["0162-8828","2160-9292","1939-3539"]},"language":[{"iso":"eng"}],"keyword":["Automated Machine Learning","Multi Label Classification","Hierarchical Planning","Bayesian Optimization"],"user_id":"5786","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"_id":"21004","status":"public","abstract":[{"text":"Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.","lang":"eng"}],"type":"journal_article","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence"}]