Smart Contract with Machine Learning for Multi-objective Optimization in Manufacturing Quality Control

Abstract

Blockchain can be used to store data safely while ensuring data transparency in quality control of advanced manufacturing. A smart contract running on blockchain can prevent data from being tampered with, along with specifying data transmission rules efficiently and securely. This paper proposes a smart contract system for manufacturing quality control that encompasses machine learning to solve dynamic multi-objective combinatorial optimization problem in production. The proposed system was experimented in various production scenarios. Experimental results showed that the system can effectively restore data through smart contracts when the data were artificially tampered with. In addition, the machine learning algorithm can improve the efficiency of the productions and achieve the combined optimization of multiple objectives.