Penetration Depth Modeling and Process Parameter Maps for Laser Welds Using Machine Learning
Journal of Welding and Joining. 2021;39(4):392-401 
Modeling of Laser Welds Using Machine Learning Algorithm Part I: Penetration Depth for Laser Overlap Al/Cu Dissimilar Metal Welds
Journal of Welding and Joining. 2021;39(1):27-35
Laser Powder Bed Fusion Parameter Selection via Machine-Learning-Augmented Process Modeling
JOM. 2020;72(12):4393-4403
Optimization of Process Variables for Prediction of Penetration Depth of HSLA Steel Welds Using Response Surface Methodology
Key Engineering Materials. 2022;934:119-128
Prediction and experimental validation of penetration depth of butt welds in thin plates using superimposed laser sources
NDT & E International. 2012;50:10-19
In-process prediction of weld penetration depth using machine learning-based molten pool extraction technique in tungsten arc welding
Journal of Intelligent Manufacturing. 2022;
Modeling of Laser Welds Using Machine Learning Algorithm Part II: Geometry and Mechanical Behaviors of Laser Overlap Welded High Strength Steel Sheets
Journal of Welding and Joining. 2021;39(1):36-44
Investigation of the influences of the process parameters on the weld depth in laser beam welding of AA6082 using machine learning methods
Procedia CIRP. 2020;94:702-707
Process parameter optimization of 6061AA Friction Stir Welded Joints using Supervised Machine Learning Regression-based Algorithms
. 2021;
Process parameter optimization of 6061AA Friction Stir Welded Joints using Supervised Machine Learning Regression-based Algorithms
. 2021;