Defective Product Classification System for Smart Factory Based on Deep Learning

Huy Toan Nguyen,Nu-Ri Shin,Jin-Young Kim,Gwang-Huyn Yu,Gyeong-Ju Kwon,Woo-Young Kwak

doi:10.3390/electronics10070826

Abstract

Smart factories merge various technologies in a manufacturing environment in order to improve factory performance and product quality. In recent years, these smart factories have received a lot of attention from researchers. In this paper, we introduce a defective product classification system based on deep learning for application in smart factories. The key component of the proposed system is a programmable logic controller (PLC) artificial intelligence (AI) embedded board; we call this an AI Edge-PLC module. A pre-trained defective product classification model is uploaded to a cloud service from where the AI Edge-PLC can access and download it for use on a certain product, in this case, electrical wiring. Next, we setup the system to collect electrical wiring data in a real-world factory environment. Then, we applied preprocessing to the collected data in order to extract a region of interest (ROI) from the images. Due to limitations on the availability of appropriate labeled data, we used the transfer learning method to re-train a classification model for our purposes. The pre-trained models were then optimized for applications on AI Edge-PLC boards. After carrying out classification tasks, on our electrical wire dataset and on a previously published casting dataset, using various deep neural networks including VGGNet, ResNet, DenseNet, and GoogLeNet, we analyzed the results achieved by our system. The experimental results show that our system is able to classify defective products quickly with high accuracy in a real-world manufacturing environment.

Highlights

4.0); one of the characteristics of Industry 4.0 is the boosted productivity and increased efficiency that will be seen in factories [1,2] and achieved by modern technologies such as the internet of thing (IoT), artificial intelligence (AI), cloud computing, robotics, sensors, and integrated systems
In manufacturing environment, the thelighting lighting condition obviously changes over time leading toclassification classification erenvironment, the lighting condition obviously changes over time leading to classification ronment, condition obviously changes over time leading to errors if the vision system relies on stable lighting part, we investigate errors if the vision system relies on stable conditions
region of interest (ROI) extraction based on the S-channel data achieved a success rate of up to 96.48% correctly cropped images

Summary

Introduction

4.0); one of the characteristics of Industry 4.0 is the boosted productivity and increased efficiency that will be seen in factories [1,2] and achieved by modern technologies such as the internet of thing (IoT), artificial intelligence (AI), cloud computing, robotics, sensors, and integrated systems. The smart factory is a step beyond traditional automated manufacturing environments to factories where we will see fully automotive systems in which the machines are connected with sensors and other devices via wired or wireless networks and controlled by advanced computational intelligence [3]. Among the various kinds of sensor systems found in factories, vision-based systems are the most popular and effective, when it comes to estimating and classifying product quality. A comprehensive review of automated vision-based defect detection approaches that look at numerous kinds of materials such as ceramics, textiles, and metals were introduced in [6]

Methods

Results

Conclusion