Intelligent Production Decision-Making System for Injection Molding Factories

Abstract

This paper addresses the intelligent transformation needs of the traditional injection molding industry by proposing a comprehensive intelligent production decision-making system integrating smart scheduling, auxiliary decision-making, and digital twin simulation. By combining the Weighted Shortest Processing Time (WSPT) algorithm, dynamic programming, and equipment health assessment, the system achieves dynamic multi-machine scheduling and resource optimization. An injection molding industry-specific large model is constructed, leveraging real-time data analysis and a knowledge base to optimize process parameters and enable anomaly early warning. A digital twin platform is developed using Unity and OPC UA protocols, employing multi-physics simulation and Markov decision processes to predict production issues. The research provides a practical technical solution for the intelligent upgrading of injection molding factories, offering both theoretical innovation and engineering application value.