Department of Mechanical Engineering, Faculty of Engineering, Sanyo-Onoda City University, Japan

Biography: Fusaomi Nagata received the B.E. degree from the Department of Electronic Engineering at Kyushu Institute of Technology in 1985, and the D.E. degree from the Faculty of Engineering Systems and Technology at Saga University in 1999. He was a research engineer with Kyushu Matsushita Electric Co. from 1985 to 1988, and a special researcher with Fukuoka Industrial Technology Centre from 1988 to 2006. He is currently a professor at the Department of Mechanical Engineering, Faculty of Engineering, Tokyo University of Science, Yamaguchi, Japan and also a Dean of the Faculty of Engineering. His research interests include deep convolutional neural networks for visual inspection of resin molded articles, intelligent control for industrial robots and its application to machining process, e.g., robot sander, mold polishing robot, desktop NC machine tool with compliance control capability, machining robot with robotic CAM system, and 3D printer-like data interface for a machining robot have been developed for wood material, aluminum PET bottle mold, LED lens mold, foamed polystyrene, and so on.

Speech Title: Applications for Defect and Anomaly Detections Using Convolutional Neural Networks, Support Vector Machines and Frequency Analysis

Abstract: The authors already developed a user-friendly design and training application tool for deep convolutional neural networks (DCNNs) and support vector machines (SVMs). The application allows students and novice engineers to easily design and train DCNNs and SVMs even if they are not familiar with the software development using C++ or Python. For example, DCNNs generally have several blocks consisting of convolutional, ReLU and pooling layers to accept image files (or feature maps) and produce more characterized ones to the following latter hidden layers, which finally lead to fully-connected layers and a softmax function layer for output. Users can easily design such a DCNN by using the tool.

In this presentation, first of all, a binary classification method using DCNNs, SVMs and template matching techniques is introduced for the defect detection of resin molded articles with various small defects. Two types of SVM structures are firstly designed using the application, then they are trained using typical OK images without any defect in order to be able to distinguish images including defects from other normal images. It is assumed that the defects are crack, burr, protrusion, chipping, spot, fracture, etc. which appear in the manufacturing process of resin moulded articles. Two types of pretrained DCNNs, i.e., our proposed sssNet and well-known Alexnet, are severally incorporated into the fore parts of the two SVMs as feature extractors, in which convoluted feature vectors extracted by the DCNNs are used as input vectors to the SVMs. The performances of the SVMs incorporated with the two types of DCNNs are shown through training and classification experiments. In addition, a template matching technique is further applied to the SVM using AlexNet to narrow important target areas from original training and test images. This enhances the reliability and accuracy for binary classification using the SVM.

Besides the defect detection, an application using frequency analysis techniques is also introduced for anomaly detection of an NC machine tool for woodworking. The method of the frequency analysis is called the spectrogram, which provides the function of visual representation of spectrums with the frequency, time and strength. The spectrogram can be obtained based on the short-time Fourier transform (STFT), in which the short term sampling period can be changed, e.g., from one to ten seconds. It is assumed that the anomaly phenomena include unexpected sounds and vibrations due to, e.g., undesirable chipping of a router bit and/or variation of cutting force. The effectiveness and feasibility for anomaly detection of the NC machine tool are shown.

Keywords: Convolutional neural network, Support vector machine, Template matching, Defect detection of resin molded articles, Spectrogram, Anomaly detection of NC machine tool