다중 블록 2D-컨볼루션 신경망을 이용한 효율적인 3D 프린터 출력 결함 분류 기법

Abstract

This paper proposes a novel fault classification method with an efficient deep learning (DL) model with fast inference time and lower computational complexity during the 3D printer printing process. Specifically, a multi-block 2D-convolutional neural network (CNN) is used to classify the 3D printer fault. In the proposed method, blocks of CNNs are used to extract the features from an image dataset that is gathered with a FDM 3D printer type. The performance evaluation of the proposed model is compared with existing image classification algorithms, such as MobileNet, AlexNet, VGG-11, and VGG-16. The results show that the proposed multi-block CNN classification model yields high accuracy with 67.01% faster inference time, 87.56% lower memory usage, and lower trainable parameters up to 93.36%. Furthermore, the proposed 3D model can provide an accurate classification in real-time monitoring conditions.