Multistage quality control in manufacturing process using blockchain with machine learning technique

Abstract

Information security has more demand for digital technology. Every industry transfers its data through computer networks for legal communication. The Internet of Things (IoT), sensor-based, is one of the most current advanced tools that can handle appropriate measures to control data operations across manufacturing industries. The demand for predictive machine reliability and quality drives the development of intelligent manufacturing technologies. To this goal, a variety of machine learning algorithms are being studied. Data protection and monitoring is also another concern that is a critical component of the organization. To overcome these issues, the proposed method uses Blockchain Technology (BCT) and Machine Learning to secure the information operations and manage a dataset. Significant data approaches were employed to organize and evaluate the obtained dataset. BCT allows collecting sensor user access data, whereas ML classifiers distinguish between normal and malicious behavior to detect attacks. DoS, DDoS, intrusion, a man in the middle (MitM), brute force, cross-site scripting (XSS), and searching are the attacks detected by BCT. Furthermore, the hybrid prediction technique assessed the fault detection prediction component. The program's quality control was set using non-linear machine learning techniques that represented the complicated world and determined the actual positive rate of the standard control methodology used by the platform. The experimental result shows that the proposed method outperforms empirical metrics such as accuracy, precision, recall, and response time. The proposed method efficiently provides security between innovative manufacturing transactions.