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Penetration Depth Modeling and Process Parameter Maps for Laser Welds Using Machine Learning |
Bum-su Go, Hyeonjeong You, Hee-seon Bang, Cheolhee Kim |
J Weld Join. 2021;39(4):392-401. Published online 2021 August 11 DOI: https://doi.org/10.5781/JWJ.2021.39.4.7 |
Penetration Depth Modeling and Process Parameter Maps for Laser Welds Using Machine Learning Modeling of Laser Welds Using Machine Learning Algorithm Part I: Penetration Depth for Laser Overlap Al/Cu Dissimilar Metal Welds Laser Ultrasonic Sensing of Penetration Depth in Robotic Welding: Simulated Solidified Welds Laser Powder Bed Fusion Parameter Selection via Machine-Learning-Augmented Process Modeling Optimization of Process Variables for Prediction of Penetration Depth of HSLA Steel Welds Using Response Surface Methodology Prediction and experimental validation of penetration depth of butt welds in thin plates using superimposed laser sources In-process prediction of weld penetration depth using machine learning-based molten pool extraction technique in tungsten arc welding Modeling of Laser Welds Using Machine Learning Algorithm Part II: Geometry and Mechanical Behaviors of Laser Overlap Welded High Strength Steel Sheets Investigation of the influences of the process parameters on the weld depth in laser beam welding of AA6082 using machine learning methods Process parameter optimization of 6061AA Friction Stir Welded Joints using Supervised Machine Learning Regression-based Algorithms |
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