---
_id: '29803'
abstract:
- lang: eng
  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."
author:
- first_name: Michael
  full_name: Hesse, Michael
  id: '29222'
  last_name: Hesse
- first_name: Matthias
  full_name: Hunstig, Matthias
  last_name: Hunstig
- first_name: Julia
  full_name: Timmermann, Julia
  id: '15402'
  last_name: Timmermann
- first_name: Ansgar
  full_name: Trächtler, Ansgar
  id: '552'
  last_name: Trächtler
citation:
  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.'
  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} }'
  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.
  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.'
conference:
  end_date: 2022-02-05
  location: Online
  name: 11th International Conference on Pattern Recognition Applications and Methods
  start_date: 2022-02-03
date_created: 2022-02-09T12:50:25Z
date_updated: 2024-11-13T08:44:17Z
department:
- _id: '153'
- _id: '880'
keyword:
- Bayesian optimization
- Wire bonding
- Feed-forward control
- model-free design
language:
- iso: eng
page: 383-394
publication: Proceedings of the 11th International Conference on Pattern Recognition
  Applications and Methods (ICPRAM)
publication_identifier:
  isbn:
  - 978-989-758-549-4
quality_controlled: '1'
status: public
title: Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward
  Control Design
type: conference
user_id: '82875'
year: '2022'
...
---
_id: '21004'
abstract:
- lang: eng
  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.'
author:
- first_name: Marcel Dominik
  full_name: Wever, Marcel Dominik
  id: '33176'
  last_name: Wever
  orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
  full_name: Tornede, Alexander
  id: '38209'
  last_name: Tornede
- first_name: Felix
  full_name: Mohr, Felix
  last_name: Mohr
- first_name: Eyke
  full_name: Hüllermeier, Eyke
  id: '48129'
  last_name: Hüllermeier
citation:
  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>'
  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>'
  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} }'
  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>.'
  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>.'
  short: M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern
    Analysis and Machine Intelligence (2021) 1–1.
date_created: 2021-01-16T14:48:13Z
date_updated: 2022-01-06T06:54:42Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
doi: 10.1109/tpami.2021.3051276
keyword:
- Automated Machine Learning
- Multi Label Classification
- Hierarchical Planning
- Bayesian Optimization
language:
- iso: eng
page: 1-1
project:
- _id: '1'
  name: SFB 901
- _id: '3'
  name: SFB 901 - Project Area B
- _id: '10'
  name: SFB 901 - Subproject B2
- _id: '52'
  name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
  issn:
  - 0162-8828
  - 2160-9292
  - 1939-3539
publication_status: published
status: public
title: 'AutoML for Multi-Label Classification: Overview and Empirical Evaluation'
type: journal_article
user_id: '5786'
year: '2021'
...