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Machine learning-driven multi-objective parameter optimization for sustainable, efficient, and high-quality ultrasonic wire bonding.

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Title: Machine learning-driven multi-objective parameter optimization for sustainable, efficient, and high-quality ultrasonic wire bonding.
Authors: Buchner, Christoph1,2 (AUTHOR) christoph.buchner@strama-mps.de, Riedle, Benjamin1 (AUTHOR) benjamin.riedle@strama-mps.de, Krauß, Jonas3 (AUTHOR) jonas.krauss@ipa.fraunhofer.de, Seidler, Christian T.4 (AUTHOR) christian.seidler@ipa.fraunhofer.de, Huber, Marco F.2,5 (AUTHOR) marco.huber@ieee.org, Eigenbrod, Hartmut5 (AUTHOR) hartmut.eigenbrod@ipa.fraunhofer.de, von Ribbeck, Hans-Georg6 (AUTHOR) hans-georg.vonribbeck@de.fkdelvotec.com, Schlicht, Franz6 (AUTHOR) franz.schlicht@de.fkdelvotec.com
Source: Journal of Intelligent Manufacturing. Apr2026, Vol. 37 Issue 4, p1681-1699. 19p.
Database: Business Source Ultimate
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s10845-025-02615-3
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      – Code: eng
        Text: English
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            – D: 01
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              Text: Apr2026
              Type: published
              Y: 2026
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            – TitleFull: Journal of Intelligent Manufacturing
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