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

Source: [Computer Vision Algorithm for Predicting the Welding Efficiency of Friction Stir Welded Copper Joints from its Microstructures](<https://www.sota2.com/research/paper/68fa6c111e209131cea31df0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:02:49.336Z

Authors: Akshansh Mishra, Asmita Suman, Devarrishi Dixit

Published: 2022-03-16T14:04:52.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2203.09479>)

[PDF](<https://arxiv.org/pdf/2203.09479.pdf>)