Thermal Image Classification Using Convolutional Neural Network (CNN) For Thermal Stress Prediction In Metal 3D Printing

Abstract

In this paper, a neural network strategy consisting of Convolutional Neural Networks (CNNs) is introduced to classify thermal stress in metal additive manufacturing. Past thermal images of a Directed Laser Metal Deposition (DLMD) process were pre-processed and labelled depending on the determined values of thermal stress. Three CNNs, DenseNet201, MobileNetV2, and ResNet50, were tested in two scenarios: as feature extractors, combined with Support Vector Machine (SVM) classifier and end-to-end training with ADAM optimizer. The experimental performance indicated that MobileNetV2 fared the best at attaining the highest accuracy (96.36 %) since it has a less resource-hungry framework and a quicker convergence. The model also performed high generalization when validated on unseen data through both individual and batch validation. This paper shows how it is possible to combine real-time thermal inspection and a CNN to automatically determine defects in metal 3D printing.