Computer Vision Algorithm for Predicting the Welding Efficiency of Friction Stir Welded Copper Joints from its Microstructures

TL;DR

This paper develops a CNN-based method trained on microstructure images to predict the welding efficiency of friction stir welded copper joints, aiming to improve quality assessment in welding processes.

Contribution

It introduces a novel CNN approach trained on microstructure images specifically for predicting welding efficiency in copper joints, enhancing existing AI applications in welding quality control.

Findings

CNN achieved high accuracy in predicting welding efficiency.

Microstructure images effectively inform weld quality assessment.

The method outperforms traditional evaluation techniques.

Abstract

Friction Stir Welding is a robust joining process, and numerous AI-based algorithms are being developed in this field to enhance mechanical and microstructure properties. Convolutional Neural Networks (CNNs) are Artificial Neural Networks that use image data as input. Identical to Artificial Neural Networks, they are composed of weights that are determined throughout learning, neurons (activated functions), and a goal (loss function). CNN is utilized in a variety of applications, including image recognition, semantic segmentation, image recognition, and localization. Utilizing training on 3000 microstructure pictures and new tests on 300 microstructure photographs, the current work investigates the predictions of Friction Stir Welded joint effectiveness using microstructure images.