## 1 Introduction
### Related Work
[13] proposed a multi-agent deep RL approach using deep Q-networks (DQNs) in textile manufacturing, which demonstrated improvements in product quality, productivity, and cost efficiency through a case studyon the ozonation process. In the domain of real-time laser welding, [PERSON] et al. [14] employed a Q-learning-based control system that used sensor feedback to regulate weld quality, achieving target performance without relying on surrogate models. In machining operations, [PERSON] et al. [15] used a Proximal Policy Optimization (PPO)-based RL algorithm to optimize cutting speed, feed rate, and depth with respect to tool wear, resulting in a 7.6% reduction in energy consumption and an 8.6% decrease in production time. Similarly, [PERSON] et al. [16] integrated Graph Neural Networks with multi-agent RL for camshaft grinding optimization; however, their model was not validated using real-world data. Beyond traditional manufacturing, RL has also been adopted in process control applications. [PERSON] et al. [17] applied the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to emulsion polymerization, achieving better copolymer composition and reduced reaction times. In parallel, [PERSON] and [PERSON] [18] utilized neural networks to optimize multiple objectives in CNC milling for furniture manufacturing. While effective, their static architecture lacked the dynamic adaptation capabilities of RL systems, which can adjust policies in real-time based on environmental feedback. Hybrid modeling approaches also show potential. [PERSON] et al. [19] combined Finite Element Method (FEM) simulations with neural networks to optimize the design of traditional Montenegrin furniture, highlighting the benefits of integrating physics-based and data-driven models. While RL-based methods have demonstrated strong results in single-objective manufacturing optimization tasks, significant research gaps remain in the development of RL systems capable of simultaneously balancing competing quality criteria. Addressing these challenges is essential for advancing RL applications in multi-objective industrial optimization scenarios. In gear manufacturing, numerous studies have focused on optimizing process and tool design to enhance production quality and efficiency. Although relatively few works specifically address pinion gear manufacturing, this study considers pinions within the broader context of gear systems due to their shared principles, such as hobbing, while also recognizing key distinctions in geometry, function, and process parameters. [PERSON] and [PERSON] [20] optimized the heat treatment process for pinion gears using the Taguchi method, identifying optimal settings of a 910 \({}^{\circ}\)C furnace temperature and 60 min quenching time to improve surface hardness and consistency for automotive applications. [PERSON] et al. [21] applied predictive modeling techniques to optimize hobbing parameters for gear production. Similarly, [PERSON] et al. [22] proposed a hybrid approach combining K-means clustering, Multi-Objective Hunger Games Search (MOHGS), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to improve gear hobbing performance. [PERSON] et al. [23] employed genetic algorithms (GAs) for gear parameter optimization. All of these studies rely on offline optimization approaches. [PERSON] et al. [24] used BevelCut simulation to optimize hypeoid gear designs including pinions by analyzing cutting characteristics such as chip thickness and distribution. However, their work did not incorporate data-driven or machine learning-based methods. This article advances the application of RL in manufacturing by addressing the challenges of multi-objective optimization. A use case involving dynamic optimization in pinion manufacturing is presented, demonstrating the potential of RL in real-world production settings. To the best of the authors' knowledge, this study represents the first application of a multi-agent RL-based optimization framework specifically in the context of pinion gear manufacturing.