首页|Research from Technical University Munich (TU Munich) YieldsNew Findings on Mac hine Learning (Acoustic process monitoringduring the laser beam welding of stai nless-steel foils using an adjustablering mode laser beam source)
Research from Technical University Munich (TU Munich) YieldsNew Findings on Mac hine Learning (Acoustic process monitoringduring the laser beam welding of stai nless-steel foils using an adjustablering mode laser beam source)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Research findings on artificial intell igence are discussed in a new report. Accordingto news reporting originating fr om Technical University Munich (TU Munich) by NewsRx correspondents,research st ated, “Electrification of the mobility sector is vital to meet the targets for r educing greenhousegas emissions. Besides battery-based mobility solutions, poly mer electrolyte membrane fuel cells (PEMFCs)are a promising technology for elec trifying drive trains, especially in heavy-duty applications, such asmaritime o r logistics.”Funders for this research include Bundesministerium Fur Bildung Und Forschung.The news journalists obtained a quote from the research from Technical Universit y Munich (TUMunich): “Bipolar plates, a key component of PEMFCs, can consist of two stainless-steel foils that mustbe welded to be gas-tight. In order to join the two metal foils, laser beam welding is the state-of-the-arttechnology. Cur rent challenges include process instabilities at higher welding speeds, such as the humpingeffect, which can cause weld seam imperfections. Therefore, applying sensors for laser beam welding is apromising approach to monitor the welding p rocess. AISI 316L foils were welded within the scope of thiswork with various p rocess parameters using an adjustable ring mode laser beam source. Additionally, anoptical microphone was used as a process monitoring system. By applying diff erent parameter settingsand due to the introduction of artificial faults, weld seam defects, such as a burn-through or a gap, wereinduced.”
Technical University Munich (TU Munich)CyborgsEmergingTechnologiesMachine LearningStainless SteelTechnology
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