Providing Security and Managing Quality Through Machine Learning Techniques for an Image Processing Model in the Industrial Internet of Things

Abstract

Currently, the transfer of defect-free, high-quality products is a significant factor for success in the manufacturing industries. The Internet of Things (IoT) has become a major part of day-to-day life. The IoT links physical things in the environment, and these things are controlled with the aid of sensors. A sensor is a component used to determine the physical property of an element, any procedures or modifications observed in the environment, as well as send the information to other devices such as computers. Numerous research studies are being conducted on the sensors used in IoT systems to provide smart services and knowledge for smart manufacturing and industry. Creating a system which aids in controlling the quality that enhances competence and production speed by eliminating abnormal products automatically is of the utmost importance. The industrial image processing model uses special cameras installed within the production line. The proposed model can be used to efficiently computerize the structure of the CPU production lines in an industry, for example, outcome images of production lines are scanned, some structure abnormalities are noted by the system and data are sent to the system administrator through cloud system networks. Here machine learning techniques are proposed for suitable classification. Machine learning is a way of making computers learn from the data of previous experiences. It helps machines have the capability of learning as well as improving from past experiences without having to be programmed every time. The main focus is on abnormalities and security concerns regarding sending data to the cloud.