A Deep Reinforcement Learning (DRL) Decision Model for Heating Process Parameters Identification in Automotive Glass Manufacturing

Abstract

This research investigates the applicability of Deep Reinforcement Learning (DRL) to control the heating process parameters of tempered glass in industrial electric furnace. In most cases, these heating process parameters, also called recipe, are given by a trial and error procedure according to the expert process experience. In order to optimize the time and the cost associated to this recipe choice, we developed an offline decision system which consists of a deep reinforcement learning framework, using Deep Q-Network (DQN) algorithm, and a self-prediction artificial neural network model. This decision system is used to define the main heating parameters (the glass transfer speed and the zone temperature) based on the desired outlet temperature of the glass, and it has the capacity to improve its performance without further human assistance. The results show that our DQN algorithm converges to the optimal policy, and our decision system provides good recipe for the heating process with deviation not exceeding process limits. To our knowledge, it is the first demonstrated usage of deep reinforcement learning for heating process of tempered glass specifically and tempering process in general. This work also provides the basis for dealing with the problem of energy consumption during the tempering process in electric furnace.

Enhancing Smart Contract Security Through Multi-Agent Deep Reinforcement Learning Fuzzing: A Survey of Approaches and Techniques

“…Incorporating DRL in security testing has transformed vulnerability detection in software systems, providing better performance than traditional methods [22]. By training agents to explore program behaviors and identify security flaws intelligently, DRL-based approaches have shown significant success in uncovering vulnerabilities in deep neural networks, smart contracts, and other critical software applications [23], [24]. This application highlights the potential of DRL in enhancing cybersecurity measures and strengthening software systems against malicious exploits [25].…”