Abstract
Smart manufacturing systems are growing based on the various requests for predicting the reliability and quality of equipment. Many machine learning techniques are being examined to that end. Another issue which considers an important part of industry is data security and management. To overcome the problems mentioned above, we applied the integrated methods of blockchain and machine learning to secure system transactions and handle a dataset to overcome the fake dataset. To manage and analyze the collected dataset, big data techniques were used. The blockchain system was implemented in the private Hyperledger Fabric platform. Similarly, the fault diagnosis prediction aspect was evaluated based on the hybrid prediction technique. The system’s quality control was evaluated based on non-linear machine learning techniques, which modeled that complex environment and found the true positive rate of the system’s quality control approach.
Highlights
The smart manufacturing system movement in recent years has been moving toward integrating blockchain technology, physics, and cyber capabilities to capture their advantages, and toward using detailed information to expand system-wide flexibility and compatibility [1,2]
Based on the modern technologies used for machine learning—Internet of Things (IoT), big data, etc.—in smart manufacturing, the industry’s main focus is creating an intelligent manufacturing environment
The predictive analysis of machine learning algorithms, evaluation metrics, blockchain execution results, and smart manufacturing vision is presented in detail
Summary
The smart manufacturing system movement in recent years has been moving toward integrating blockchain technology, physics, and cyber capabilities to capture their advantages, and toward using detailed information to expand system-wide flexibility and compatibility [1,2]. The main focus of smart manufacturing in Industry 4.0 is to qualify the relationship between different manufacturing units, facilities, retailers, etc., for further support manufacturing industries, based on the total manufacturing value chain [12] This process affects automating and optimizing the operations, improving flexibility, safety, cost reduction, productivity, and profitability. Applying big data techniques to manage the massive manufacturing dataset This remainder of the paper is divided as follows: Section 2 presents the practical literature review of the current industrial and technological processes.
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