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    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>8</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>38</volume>
    <type>article</type>
    <publisherName>Laser Institute of America</publisherName>
    <publisherPlace/>
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    <title language="eng">Prediction of weld pool and keyhole geometries in high-power laser beam welding through a physics-informed generative artificial intelligence approach</title>
    <abstract language="eng">The weld pool and keyhole geometries are critical characteristics in evaluating the stability of the high-power laser beam welding (LBW) process and determining the resultant weld quality. However, obtaining these data through experimental or numerical methods remains challenging due to the difficulties in experimental measurements and the high computational demands of numerical modelling. This paper presents a physics-informed generative approach for predicting weld pool and keyhole geometries in the LBW process. With the help of a well experimentally validated numerical model considering the underlying physics in the LBW, the geometries of the weld pool and keyhole under various welding conditions are calculated, serving as the dataset of the generative model. A conditional variational autoencoder model is employed to generate realistic 2D weld pool and keyhole geometries from the welding parameters. We utilize a β-variational autoencoder model with the evidence lower bound loss function and include Kullback–Leibler divergence annealing to better optimize model performance and stability during training. The generated results show a good agreement with the ground truth from the numerical simulation. The proposed approach exhibits the potential of physics-informed generative models for a rapid and accurate prediction of the weld pool geometries across a diverse range of process parameters, offering a computationally efficient alternative to full numerical simulations for process optimization and control in laser beam welding processes.</abstract>
    <parentTitle language="eng">Journal of Laser Applications</parentTitle>
    <identifier type="issn">1042-346X</identifier>
    <identifier type="doi">10.2351/7.0001862</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-656621</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Xiangmeng Meng</author>
    <author>Marcel Bachmann</author>
    <author>Pascal Kising</author>
    <author>Fan Yang</author>
    <author>Michael Rethmeier</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser beam welding</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Generative artificial intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Numerical simulation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Weld pool</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Keyhole dynamics</value>
    </subject>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">9 Komponentensicherheit</collection>
    <collection role="institutes" number="">9.3 Schweißtechnische Fertigungsverfahren</collection>
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  </doc>
  <doc>
    <id>65681</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>15</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>1342</volume>
    <type>article</type>
    <publisherName>IOP Publishing</publisherName>
    <publisherPlace/>
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    <title language="eng">Digitalisation and Carbon Footprint Evaluation of Gas Metal Arc Welding and Wire-Arc Directed Energy Deposition</title>
    <abstract language="eng">This paper showcases how a holistic approach to digitalisation enables data-driven welding applications, exemplarily for a gas metal arc welding (GMAW) laboratory. The workflow integrates advanced process monitoring, synchronised multi-sensor data acquisition and tools for data analytics. A welding domain-specific data exchange format weldx is presented that unifies and aggregates the data sets acquired during process monitoring with final component quality metrics, supporting reuse, traceability, and reproducibility. Two case studies illustrate the approach. First, GMAW parameters are adaptively adjusted according to local seam geometry to compensate joint-preparation deviations from nominal values typical for large-scale steel fabrication. Second, the seamless data aggregation along the welding production chain enables an automatic life-cycle assessment (LCA), quantifying the environmental impacts of additive manufacturing with DED Arc/M and attributing the dominant contributors to the carbon footprint. Collectively, the results indicate that a fully integrated experimental set-up together with standardised data structures and scalable analytics can couple monitoring, control, and sustainability, thereby realising the potential of digitalisation for high-quality and environmentally informed welding production.</abstract>
    <parentTitle language="eng">IOP Conference Series: Materials Science and Engineering</parentTitle>
    <identifier type="issn">1757-8981</identifier>
    <identifier type="doi">10.1088/1757-899X/1342/1/012067</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-656812</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Çağtay Fabry</author>
    <author>Andreas Pittner</author>
    <author>Alexander Kaplan</author>
    <author>Michael Rethmeier</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digitalisation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Arc welding</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>DED-arc</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Life Cycle Assessment</value>
    </subject>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">9 Komponentensicherheit</collection>
    <collection role="institutes" number="">9.3 Schweißtechnische Fertigungsverfahren</collection>
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    <collection role="themenfelder" number="">Degradationsmechanismen</collection>
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    <file>https://opus4.kobv.de/opus4-bam/files/65681/Fabry2026a - Digitalisation and Carbon Footprint Evaluation of GMAW and DED-Arc.pdf</file>
  </doc>
  <doc>
    <id>65701</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>10</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>13</volume>
    <type>article</type>
    <publisherName>Elsevier B.V.</publisherName>
    <publisherPlace/>
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    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
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    <title language="eng">Investigation on seam quality and fatigue crack propagation in electron beam welded additive manufactured Inconel 939</title>
    <abstract language="eng">Laser powder bed fusion of metals (PBF-LB/M) offers great potential for the production of new and spare parts for stationary gas turbines made of nickel superalloys such as Inconel 939 (IN939). In order to enable integration into existing assemblies and overcome design limitations, the additive manufacturing process chain must be expanded by suitable joining techniques. This study compares the electron beam welding of cast IN939 sheets and sheets produced additively using PBF-LB/M. The investigation focuses on the achievable seam quality with regard to geometric irregularities and internal defects in the form of liquation cracks on the microscale in the heat-affected zone. The evaluation of the welded samples shows no differences in the formation of the seam shape between the additively manufactured material and the cast material. For both materials, the highest quality category for beam-welded seams according to DIN EN ISO 13,919–1 was achieved at high welding speeds of 20 mm/s. Regardless of the manufacturing method, both materials show an increase in crack formation with increasing welding speed. However, due to its microstructure, the PBF-LB/M material exhibits significantly fewer microcracks overall. Final crack propagation tests on welded PBF-LB/M samples that were treated using HIP also show stable crack growth without sudden failure, which opens up potential for practical application.</abstract>
    <parentTitle language="eng">Journal of Advanced Joining Processes</parentTitle>
    <identifier type="issn">2666-3309</identifier>
    <identifier type="doi">10.1016/j.jajp.2026.100389</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-657015</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>J. Raute</author>
    <author>J. Schmidt</author>
    <author>A. Bhadeliya</author>
    <author>B. Rehmer</author>
    <author>J. Olbricht</author>
    <author>T. Jokisch</author>
    <author>M. Biegler</author>
    <author>M. Rethmeier</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Electron beam welding</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Inconel 939</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>PBF-LB/M</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Liquation cracking</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fatigue crack propagation</value>
    </subject>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">5 Werkstofftechnik</collection>
    <collection role="institutes" number="">5.2 Metallische Hochtemperaturwerkstoffe</collection>
    <collection role="institutes" number="">9 Komponentensicherheit</collection>
    <collection role="institutes" number="">9.3 Schweißtechnische Fertigungsverfahren</collection>
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    <collection role="fulltextaccess" number="">Datei für die Öffentlichkeit verfügbar ("Open Access")</collection>
    <collection role="unnumberedseries" number="">Wissenschaftliche Artikel der BAM</collection>
    <collection role="institutes" number="">9.6 Additive Fertigung metallischer Komponenten</collection>
    <collection role="themenfelder" number="">Additive Fertigung</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/65701/Raute 2026 Investigation on seam quality and fatigue crack propagation.pdf</file>
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    <id>65758</id>
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    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>12</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>74</volume>
    <type>article</type>
    <publisherName>Elsevier Ltd.</publisherName>
    <publisherPlace/>
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    <contributingCorporation/>
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    <title language="eng">Porosity prediction in laser beam welding with a multimodal physics-informed machine learning framework</title>
    <abstract language="eng">Laser beam welding (LBW) of metallic components is a knowledge‑intensive manufacturing process whose quality depends on the complex multi‑physics. However, its engineering application is often hindered by the occurrence of porosity defects. Achieving a thorough understanding and reliable prediction of porosity defects remains difficult because it demands robust representation and reasoning over nonlinear and hard‑to‑observe physical information. In this study, we propose an integrated multimodal physics-informed machine learning (PIML) framework with the help of multi-physical modelling and experimental data to predict the porosity defects in laser beam welding of aluminum alloys. The whole framework contains a multimodal PIML model for predicting the porosity ratio and an ML-based estimator for relevant physical information. By utilizing the scalar welding parameters and high-dimensional physical information (probability of keyhole collapses, cumulative existing time of collapses, and molten pool geometry) as inputs, the multimodal PIML model shows great superiority in predicting the porosity ratio, with a reduction of the mean square error by 45%, compared with the ML model trained only with welding parameters. The ML-based estimator constructed with an encoder‐decoder architecture can accurately reproduce the critical physical information within a timeframe of seconds. By integrating these two ML models, the proposed framework advances engineering informatics by offering a scalable, physics-knowledge‑centric solution for fast and accurate porosity prediction in LBW manufacturing.</abstract>
    <parentTitle language="eng">Advanced Engineering Informatics</parentTitle>
    <identifier type="issn">1474-0346</identifier>
    <identifier type="doi">10.1016/j.aei.2026.104611</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Xiangmeng Meng</author>
    <author>Marcel Bachmann</author>
    <author>Fan Yang</author>
    <author>Michael Rethmeier</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser beam welding</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Porosity defect</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Physics-informed machine learning</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multimodal model</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Simulation</value>
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    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">9 Komponentensicherheit</collection>
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    <collection role="themenfelder" number="">Degradationsmechanismen</collection>
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    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/65758/1-s2.0-S1474034626003034-main.pdf</file>
  </doc>
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