Auto-Classifier: A Robust Defect Detector Based on an AutoML Head

The dominant approach for surface defect detection is the use of hand-crafted feature-based methods. However, this falls short when conditions vary that affect extracted images. So, in this paper, we sought to determine how well several state-of-the-art Convolutional Neural Networks perform in the task of surface defect detection. Moreover, we propose two methods: CNN-Fusion, that fuses the prediction of all the networks into a final one, and Auto-Classifier, which is a novel proposal that improves a Convolutional Neural Network by modifying its classification component using AutoML. We carried out experiments to evaluate the proposed methods in the task of surface defect detection using different datasets from DAGM2007. We show that the use of Convolutional Neural Networks achieves better results than traditional methods, and also, that Auto-Classifier out-performs all other methods, by achieving 100% accuracy and 100% AUC results throughout all the datasets.Comment: 12 pages, 2 figures. Published in ICONIP2020, proceedings published in the Springer's series of Lecture Notes in Computer Scienc

A Framework for Process Model Based Human-Robot Collaboration System Using Augmented Reality

Part 7: Industry 4.0 – Collaborative Cyber-physical Production and Human SystemsInternational audienceThe concept of smart factory is being applied into traditional manufacturing system. Since factory automation is applied gradationally, Human-robot collaboration (HRC) system is becoming an important issue. In order to construct an effective HRC system, clear communication with the human workers and the robots has to be considered. This research proposed a conceptual framework of process model based HRC system for efficient human-robot collaboration in a semi automation process to produce electric motors. We applied a process modeling methodology for capturing collaborative features, activity and resource flow in the manufacturing process. The model defined by the proposed methodology is the data storage to contain process information and interface between the human workers and robots to provide accurate information in the appropriate context. Furthermore, machine vision technology is implemented to recognize specifications of work in process (WIP) parts. The recognized parts are mapped with the correct work order and work instruction manual defined by the proposed model. In order to reduce human worker’s errors, the extracted work information is transmitted to the worker through the augmented reality device. The proposed HRC system is expected to be able to support the construction of a semi automation system that can reduce errors of human workers and ensure production flexibility