Deep Convolutional Neural Network Algorithm for Prediction of the Mechanical Properties of FSW Copper Welds from its Microstructure

Convolutional Neural Network (CNN) is a special type of Artificial Neural Network which takes input in the form of an image. Like Artificial Neural Network they consist of weights that are estimated during training, neurons (activation functions), and an objective (loss function). CNN is finding various applications in image recognition, semantic segmentation, object detection, and localization. The present work deals with the prediction of the welding efficiency of the Friction Stir Welded joints on the basis of microstructure images by carrying out training on 3000 microstructure images and further testing on 300 microstructure images. The loss function decreased for both training and testing set decreased with the increasing number of epochs. The obtained results showed an accuracy of 80 % on the validation dataset.