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The fusion of domain knowledge and probabilistic models in the form of hybrid models allows an efficient optimization of these processes with machine learning. This can be a key technology to realize self-learning laser-based universal machines in the future. The article discusses some examples where algorithm-based optimization, partly supported by hybrid models, can already greatly reduce the effort required to find suitable process parameters.</swrc:abstract><swrc:hasExtraField><swrc:Field swrc:value="10.1117/12.2653604" swrc:key="doi"/></swrc:hasExtraField><swrc:author><rdf:Seq><rdf:_1><swrc:Person swrc:name="Andreas Michalowski"/></rdf:_1><rdf:_2><swrc:Person swrc:name="Alexander Ilin"/></rdf:_2><rdf:_3><swrc:Person swrc:name="Alexander Kroschel"/></rdf:_3><rdf:_4><swrc:Person swrc:name="Stephanie Karg"/></rdf:_4><rdf:_5><swrc:Person swrc:name="Peter Stritt"/></rdf:_5><rdf:_6><swrc:Person swrc:name="Adina Dais"/></rdf:_6><rdf:_7><swrc:Person swrc:name="Sebastian Becker"/></rdf:_7><rdf:_8><swrc:Person swrc:name="Gerhard Kunz"/></rdf:_8><rdf:_9><swrc:Person swrc:name="Steffen Sonntag"/></rdf:_9><rdf:_10><swrc:Person swrc:name="Martin Lustfeld"/></rdf:_10><rdf:_11><swrc:Person swrc:name="Petru Tighineanu"/></rdf:_11><rdf:_12><swrc:Person swrc:name="Volkher Onuseit"/></rdf:_12><rdf:_13><swrc:Person swrc:name="Michael Haas"/></rdf:_13><rdf:_14><swrc:Person swrc:name="Thomas Graf"/></rdf:_14><rdf:_15><swrc:Person swrc:name="Heiko Ridderbusch"/></rdf:_15></rdf:Seq></swrc:author><swrc:editor><rdf:Seq><rdf:_1><swrc:Person swrc:name="Laura Gemini"/></rdf:_1><rdf:_2><swrc:Person swrc:name="Aiko Narazaki"/></rdf:_2><rdf:_3><swrc:Person swrc:name="Jan Kleinert"/></rdf:_3></rdf:Seq></swrc:editor></rdf:Description></burst:publication></item><item rdf:about="https://puma.ub.uni-stuttgart.de/bibtex/2f5c50fb27ea0fa91936ae7c3d47cfbb2/ifsw"><title>Multimodal sensor fusion with SWIR imaging and audio for inline gas-tightness monitoring in laser-welded bipolar plates</title><link>https://puma.ub.uni-stuttgart.de/bibtex/2f5c50fb27ea0fa91936ae7c3d47cfbb2/ifsw</link><dc:creator>ifsw</dc:creator><dc:date>2025-12-10T11:18:04+01:00</dc:date><dc:subject>myown peer machinelearning laserwelding sensorfusion </dc:subject><content:encoded>&lt;span data-person-type=&#034;author&#034; class=&#034;authorEditorList &#034;&gt;&lt;span&gt;&lt;span itemtype=&#034;http://schema.org/Person&#034; itemscope=&#034;itemscope&#034; itemprop=&#034;author&#034;&gt;&lt;a title=&#034;Manuel Klaiber&#034; itemprop=&#034;url&#034; href=&#034;/person/1989f18a6aeebd1e40bd4da9b9ca71c48/author/0&#034;&gt;&lt;span itemprop=&#034;name&#034;&gt;M. 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Michalowski&lt;/span&gt;&lt;/a&gt;&lt;/span&gt;&lt;/span&gt;. &lt;/span&gt;&lt;span class=&#034;additional-entrytype-information&#034;&gt;&lt;span itemtype=&#034;http://schema.org/PublicationIssue&#034; itemscope=&#034;itemscope&#034; itemprop=&#034;isPartOf&#034;&gt;&lt;em&gt;&lt;span itemprop=&#034;journal&#034;&gt;Optics &amp;amp;amp; Laser Technology&lt;/span&gt;, &lt;/em&gt;  &lt;/span&gt;(&lt;em&gt;&lt;span&gt;December 2025&lt;meta content=&#034;December 2025&#034; itemprop=&#034;datePublished&#034;/&gt;&lt;/span&gt;&lt;/em&gt;)&lt;/span&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://puma.ub.uni-stuttgart.de/tag/myown"/><rdf:li rdf:resource="https://puma.ub.uni-stuttgart.de/tag/peer"/><rdf:li rdf:resource="https://puma.ub.uni-stuttgart.de/tag/machinelearning"/><rdf:li rdf:resource="https://puma.ub.uni-stuttgart.de/tag/laserwelding"/><rdf:li rdf:resource="https://puma.ub.uni-stuttgart.de/tag/sensorfusion"/></rdf:Bag></taxo:topics><burst:publication><rdf:Description rdf:about="https://puma.ub.uni-stuttgart.de/bibtex/2f5c50fb27ea0fa91936ae7c3d47cfbb2/ifsw"><owl:sameAs rdf:resource="/uri/bibtex/2f5c50fb27ea0fa91936ae7c3d47cfbb2/ifsw"/><rdf:type rdf:resource="http://swrc.ontoware.org/ontology#Article"/><owl:sameAs rdf:resource="http://dx.doi.org/10.1016/j.optlastec.2025.113441"/><swrc:date>Wed Dec 10 11:18:04 CET 2025</swrc:date><swrc:journal>Optics &amp;amp; Laser Technology</swrc:journal><swrc:month>dec</swrc:month><swrc:pages>113441</swrc:pages><swrc:publisher><swrc:Organization swrc:name="Elsevier BV"/></swrc:publisher><swrc:title>Multimodal sensor fusion with SWIR imaging and audio for inline gas-tightness monitoring in laser-welded bipolar plates</swrc:title><swrc:volume>192</swrc:volume><swrc:year>2025</swrc:year><swrc:keywords>myown peer machinelearning laserwelding sensorfusion </swrc:keywords><swrc:hasExtraField><swrc:Field swrc:value="0030-3992" swrc:key="issn"/></swrc:hasExtraField><swrc:hasExtraField><swrc:Field swrc:value="10.1016/j.optlastec.2025.113441" swrc:key="doi"/></swrc:hasExtraField><swrc:author><rdf:Seq><rdf:_1><swrc:Person swrc:name="Manuel Klaiber"/></rdf:_1><rdf:_2><swrc:Person swrc:name="Matthias Hartmann"/></rdf:_2><rdf:_3><swrc:Person swrc:name="Jan-Patrick Hermani"/></rdf:_3><rdf:_4><swrc:Person swrc:name="Andreas Jahn"/></rdf:_4><rdf:_5><swrc:Person swrc:name="Andreas Michalowski"/></rdf:_5></rdf:Seq></swrc:author></rdf:Description></burst:publication></item></rdf:RDF>