Supervised Machine Learning Classification Algorithms for Detection of Fracture Location in Dissimilar Friction Stir Welded Joints

Machine Learning focuses on the study of algorithms that are mathematical or statistical in nature order to extract required information pattern from available data. Supervised further sub-divided into two types i.e. regression and classification algorithms. In present study, four supervised machine learning-based models Decision Trees algorithm, K- Nearest Neighbors (KNN) Support Vector Machines (SVM) Ada Boost algorithm were subjected given dataset for determination fracture location dissimilar Friction Stir Welded AA6061-T651 AA7075-T651 alloy. dataset, rotational speed (RPM), welding (mm/min), pin profile, axial force (kN) input parameters while Fracture is output parameter. The obtained results showed classified with a good accuracy score 0.889 comparison other