首页|Reports from KTH Royal Institute of Technology Add New Data to Findings in Fatig ue (Unsupervised Machine Learning for Local Stress Identification In Fatigue Ana lysis of Welded Joints)
Reports from KTH Royal Institute of Technology Add New Data to Findings in Fatig ue (Unsupervised Machine Learning for Local Stress Identification In Fatigue Ana lysis of Welded Joints)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Fatigue is the subject of a report. According to news reportingoriginating in Stockholm, Sweden, by N ewsRx journalists, research stated, “In the underlying study,a method has been proposed to automatically extract finite element (FE) peak stresses of welded components to alleviate human errors and increase the calculation accuracy. The ap proach is based onthe K-means and DBSCAN (density-based spatial clustering of a pplications with noise) methods as theunsupervised machine learning approaches. ”
StockholmSwedenEuropeCyborgsEmer ging TechnologiesFatigueHealth and MedicineMachine LearningKTH Royal Ins titute of Technology
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