Digital Twin of a Flexible Manufacturing System for Solution Preparation

Paulo Francisco Carreira Faria

Key information

Authors:

Paulo Francisco Carreira Faria (Paulo Francisco Carreira Faria)

Supervisors:

João Miguel Da Costa Sousa (João Miguel Da Costa Sousa); Susana Margarida da Silva Vieira (Susana Margarida da Silva Vieira)

Published in

December 13, 2021

Abstract

In the last decades, there has been a necessity for systems capable of handling market changes and various personalized customer needs, with near mass production efficiency, defined as mass customization. Industry 5.0 further exposed this need for robust flexible systems, as well as a necessity of manufacturing systems that work in close cooperation with workers, taking advantage of the problem-solving capabilities and knowledge of the manufacturing process. A solution for this necessity is to develop a flexible manufacturing system, capable of handling different customer requests and real-time decisions from operators. Which this thesis tackles by proposing a Digital Twin focused on the simulation of a Robot System for Solution Preparation, capable of making real-time scheduling decisions and forecasts by using a simulation model to test different resource configurations and customer requests, while allowing an operator to make changes in the processing time and order of some operations in real-time. A mixed event simulation model was utilized to do resource forecasts, where real-time decisions were performed by using completely reactive scheduling with parallel tasks. Resource forecasts were utilized to know where the manufacturing system can be improved. It was shown that combining parallel tasking with parallel machines to key processes, utilizing heuristics that emphasize the shortest transportation time and increasing the robot’s speed, best impact the performance of the system, reducing overall completion time by 82%, when comparing single tasking and single machines. The simulation model also has an animated visualization window, for a deeper understanding of the system.