Expert Twin: A Digital Twin with an Integrated Fuzzy-Based Decision-Making Module

Abstract

Digitalization and the application of modern Industry 4.0 solutions are becoming increasingly important to remain competitive as product ranges expand and global supply chains grow. This paper presents a new Digital Twin framework to achieve robustness in manufacturing process optimization and enhance the efficiency of decision support. Most digital twins in the literature synchronously represent the real system without any control elements despite the bidirectional data link. The proposed approach combines the advantages of traditional process simulations with a real-time communication and data acquisition method using programmable logic controllers designed to control automated systems. In addition, it complements this by utilizing human experience and expertise in modeling using Fuzzy Logic to create a control-enabled digital twin system. The resulting "Expert Twin" system reduces the reaction time of the production to unexpected events and increases the efficiency of decision support; it generates and selects alternatives, therefore creating smart manufacturing. The Expert Twin framework was integrated, tested, and validated on an automated production sample system in a laboratory environment. In the experimental scenarios carried out, the method production increased production line utility by up to 28% and the number of re-schedules can be halved.