Machine Learning Algorithms for Prediction of Penetration Depth and Geometrical Analysis of Weld in Friction Stir Spot Welding Process

TL;DR

This study applies machine learning algorithms to predict penetration depth and analyze weld geometry in friction stir spot welding of aluminum alloys, demonstrating high accuracy and integrating image processing for geometrical assessment.

Contribution

It introduces the use of Robust Regression for penetration depth prediction and combines machine learning with image processing for weld geometry analysis.

Findings

Robust Regression achieved a coefficient of determination of 0.96.

Support Vector Machines and Random Forest were also evaluated.

Image processing techniques effectively analyzed weld geometrical features.

Abstract

Nowadays, manufacturing sectors harness the power of machine learning and data science algorithms to make predictions for the optimization of mechanical and microstructure properties of fabricated mechanical components. The application of these algorithms reduces the experimental cost beside leads to reduce the time of experiments. The present research work is based on the prediction of penetration depth using Supervised Machine Learning algorithms such as Support Vector Machines (SVM), Random Forest Algorithm, and Robust Regression algorithm. A Friction Stir Spot Welding (FSSW) was used to join two elements of AA1230 aluminum alloys. The dataset consists of three input parameters: Rotational Speed (rpm), Dwelling Time (seconds), and Axial Load (KN), on which the machine learning models were trained and tested. It observed that the Robust Regression machine learning algorithm outperformed the rest of the algorithms by resulting in the coefficient of determination of 0.96. The research work also highlights the application of image processing techniques to find the geometrical features of the weld formation.