https://puma.ub.uni-stuttgart.de/tag/optimization%20bayesianoptimization%20peerPUMA RSS feed for /tag/optimization%20bayesianoptimization%20peer2026-04-23T06:50:48+02:00https://puma.ub.uni-stuttgart.de/bibtex/212b1bf1a3f2309bd0a79a04adff1e1ce/michaelhaasmichaelhaas2026-04-10T15:59:18+02:00Aluminum BayesianOptimization Optimization Welding X-ray myown peer <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Michael Haas" itemprop="url" href="/person/18306d0818308eb273c5e8b7e216c83de/author/0"><span itemprop="name">M. Haas</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Steinhoff" itemprop="url" href="/person/18306d0818308eb273c5e8b7e216c83de/author/1"><span itemprop="name">R. Steinhoff</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="John Powell" itemprop="url" href="/person/18306d0818308eb273c5e8b7e216c83de/author/2"><span itemprop="name">J. Powell</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Felix Zaiß" itemprop="url" href="/person/18306d0818308eb273c5e8b7e216c83de/author/3"><span itemprop="name">F. Zaiß</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Johannes Wahl" itemprop="url" href="/person/18306d0818308eb273c5e8b7e216c83de/author/4"><span itemprop="name">J. Wahl</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christian Hagenlocher" itemprop="url" href="/person/18306d0818308eb273c5e8b7e216c83de/author/5"><span itemprop="name">C. Hagenlocher</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Michalowski" itemprop="url" href="/person/18306d0818308eb273c5e8b7e216c83de/author/6"><span itemprop="name">A. Michalowski</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Journal of Intelligent Manufacturing</span>, </em> </span>(<em><span>Feb 27, 2026<meta content="Feb 27, 2026" itemprop="datePublished"/></span></em>)</span>Fri Apr 10 15:59:18 CEST 2026Journal of Intelligent ManufacturingfebEfficient parameter selection in laser welding via Bayesian optimization2026Aluminum BayesianOptimization Optimization Welding X-ray myown peer 27Identifying suitable process parameters is essential for developing effective laser welding processes. Traditionally, this involves extensive experimentation and relies heavily on expert knowledge. Bayesian optimization can be used to minimize both the experimental effort and the need for expert information. This study demonstrates the merit of Bayesian optimization for laser welding and explains the methodology for implementing the optimization technique. A strategy for selecting evaluation methods and the design of a suitable cost function to meet specific quality criteria is proposed. For the experimental demonstration, butt joint laser welding of AA1050 aluminum alloy was performed with options to adapt the laser power, welding speed, focus position, and the intensity distribution of the laser beam by changing the power distribution in a multi-core fiber. The success of the optimization was validated by finding several parameter sets producing welds that met the defined quality levels. Furthermore, the properties of the underlying surrogate model of the Bayesian optimizer generated further information that helped to improve the welding process.https://puma.ub.uni-stuttgart.de/bibtex/212b1bf1a3f2309bd0a79a04adff1e1ce/ifswifsw2026-04-10T15:59:18+02:00myown Welding peer Aluminum BayesianOptimization X-ray Optimization <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Michael Haas" itemprop="url" href="/person/18306d0818308eb273c5e8b7e216c83de/author/0"><span itemprop="name">M. Haas</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Steinhoff" itemprop="url" href="/person/18306d0818308eb273c5e8b7e216c83de/author/1"><span itemprop="name">R. Steinhoff</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="John Powell" itemprop="url" href="/person/18306d0818308eb273c5e8b7e216c83de/author/2"><span itemprop="name">J. Powell</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Felix Zaiß" itemprop="url" href="/person/18306d0818308eb273c5e8b7e216c83de/author/3"><span itemprop="name">F. Zaiß</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Johannes Wahl" itemprop="url" href="/person/18306d0818308eb273c5e8b7e216c83de/author/4"><span itemprop="name">J. Wahl</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christian Hagenlocher" itemprop="url" href="/person/18306d0818308eb273c5e8b7e216c83de/author/5"><span itemprop="name">C. Hagenlocher</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Michalowski" itemprop="url" href="/person/18306d0818308eb273c5e8b7e216c83de/author/6"><span itemprop="name">A. Michalowski</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Journal of Intelligent Manufacturing</span>, </em> </span>(<em><span>Feb 27, 2026<meta content="Feb 27, 2026" itemprop="datePublished"/></span></em>)</span>Fri Apr 10 15:59:18 CEST 2026Journal of Intelligent ManufacturingfebEfficient parameter selection in laser welding via Bayesian optimization2026myown Welding peer Aluminum BayesianOptimization X-ray Optimization 27Identifying suitable process parameters is essential for developing effective laser welding processes. Traditionally, this involves extensive experimentation and relies heavily on expert knowledge. Bayesian optimization can be used to minimize both the experimental effort and the need for expert information. This study demonstrates the merit of Bayesian optimization for laser welding and explains the methodology for implementing the optimization technique. A strategy for selecting evaluation methods and the design of a suitable cost function to meet specific quality criteria is proposed. For the experimental demonstration, butt joint laser welding of AA1050 aluminum alloy was performed with options to adapt the laser power, welding speed, focus position, and the intensity distribution of the laser beam by changing the power distribution in a multi-core fiber. The success of the optimization was validated by finding several parameter sets producing welds that met the defined quality levels. Furthermore, the properties of the underlying surrogate model of the Bayesian optimizer generated further information that helped to improve the welding process.