A Reinforcement Learning Control in Hot Stamping for Cycle Time Optimization

Abstract

Hot stamping is a hot metal forming technology increasingly in demand that produces ultra-high strength parts with complex shapes. A major concern in these systems is how to shorten production times to improve production Key Performance Indicators. In this work, we present a Reinforcement Learning approach that can obtain an optimal behavior strategy for dynamically managing the cycle time in hot stamping to optimize manufacturing production while maintaining the quality of the final product. Results are compared with the business-as-usual cycle time control approach and the optimal solution obtained by the execution of a dynamic programming algorithm. Reinforcement Learning control outperforms the business-as-usual behavior by reducing the cycle time and the total batch time in non-stable temperature phases.

A Review on Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing

“…Table 8 lists several RL techniques tailored for different optimization facets, specifically equipment, operation, and control, across diverse processes. polymerization DDPG [204] robot path planning DDPG [205] automotive hot sheet metal forming Q-learning [206] heating energy consumption DQN, PPO, AC [207] operation construction tunnel excavation DQN [208] manufacturing injection molding AC [209] RL holds significant promise for smart manufacturing, facilitating continuous refinement and optimization of production processes. By leveraging RL, industries can curtail costs, augment efficiency, and elevate product quality, concurrently reducing the product development cycle.…”