Data-driven Predictive Analysis for Smart Manufacturing Processes Based on a Decomposition Approach

Abstract

Smart Manufacturing refers to leveraging advanced analytics approaches and optimization techniques that are implemented in production operations. With the widespread increase in deploying various networked sensors in manufacturing processes, there is a progressive need for optimal and effective data management approaches. Embracing modern technologies to take advantage of manufacturing data allows us to overcome associated challenges, including real-time manufacturing process control and maintenance optimization. In line with this goal, a hybrid decomposition-based method including an evolutionary algorithm and an artificial neural network is proposed to make manufacturing smart. The proposed dynamic approach helps us obtain valuable insights for controlling manufacturing processes and gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.