Deep Learning Method based on Big Data for Defects Detection in Manufacturing Systems Industry 4.0

Due to the technological advancement in Today's manufacturing systems, a large amount of data is generated in different volume, velocity, and variety of kinds. Extracting information from these data and make a real-time decision is a big challenge to the current manufacturing systems. This study presents a novel model that converts the iFactory learning facility into a fully Industry 4.0 (I4.0) manufacturing system. To achieve this purpose, we utilized the cyber physical system (CPS) components and sensors, the Internet of Things (IoT), deep learning methods, and cloud computing to fully meet the I4.0 enablers. Cloud computing is utilised in two phases: (1) during the model training phase to hold a large amount of product image data collected from the inspection station, and (2) during the execution of the model. The core learning model is based on a convolutional neural network (CNN) that is trained from the captured product images in the production line to predict the defective items in the line. The model was initialized by Resnet method and optimized to improve the learning rate and reduced loss function. The supervised learning model achieved high accuracy prediction performance up to 96.75% in the real-time decision making process. The model was able to extract the feature map of the normal non-detective product and use it to improving the accuracy and reducing the traffic between iFactory station and the cloud server. The model exploits the parallel computing big-data framework to achieve a real time decision making. The model can be applied to the current system and adopted as with all it is functionalities for the newer systems.