Computer Vision Algorithm for Predicting the Welding Efficiency of Friction Stir Welded Copper Joints from its Microstructures
This work addresses efficiency prediction for welding engineers, but it is incremental as it applies an existing method to a new dataset.
The study tackled predicting welding efficiency of friction stir welded copper joints from microstructure images using a Convolutional Neural Network, achieving results based on training with 3000 images and testing on 300 images.
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.