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Applied Sciences
Volume 15
Issue 13
10.3390/app15137279
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Reinforcement Learning Agent for Multi-Objective Online Process Parameter Optimization of Manufacturing Processes

by
Akshay Paranjape
1,*,
Nahid Quader
1,
Lars Uhlmann
2,
Benjamin Berkels
3,
Dominik Wolfschläger
4,
Robert H. Schmitt
4,5 and
Thomas Bergs
2,5
1
IconPro GmbH, Friedlandstraße 18, 52064 Aachen, Germany
2
Manufacturing Technology Institute, MTI of RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany
3
Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen, Schinkelstrasse 2, 52062 Aachen, Germany
4
Laboratory for Machine Tools and Production Engineering, WZL of RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany
5
Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, 52074 Aachen, Germany
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(13), 7279; https://doi.org/10.3390/app15137279 (registering DOI)
Submission received: 31 May 2025 / Revised: 22 June 2025 / Accepted: 25 June 2025 / Published: 27 June 2025
(This article belongs to the Special Issue Multi-Objective Optimization: Techniques and Applications)
Versions Notes

Featured Application

This study presents a multi-objective optimization approach for manufacturing pinions, aiming to balance conflicting objectives such as geometric quality metrics related to radius and thickness. Preliminary validation is conducted using test cases, and the methodology is further substantiated through application to open-source manufacturing datasets and virtual experimentation scenarios.

Abstract

Optimizing manufacturing processes to reduce scrap and enhance process stability presents significant challenges, particularly when multiple conflicting objectives must be addressed concurrently. As the number of objectives increases, the complexity of the optimization task escalates. This difficulty is further intensified in online optimization scenarios, where optimal parameter settings must be delivered in real time within active production environments. In this work, we propose a reinforcement learning-based framework for the multi-objective optimization of manufacturing parameters, demonstrated through a case study on pinion gear manufacturing. The framework utilizes the Multi-Objective Maximum a Posteriori Optimization (MO-MPO) algorithm to train a reinforcement learning agent. A high-fidelity simulation of the pinion manufacturing process is constructed in Simufact, serving both data generation and validation purposes. The agent’s performance is assessed using a hold-out test set along with additional simulations of the physical process. To ensure the generalizability of the approach, further validation is performed using open-source manufacturing datasets and synthetically generated data. The results demonstrate the feasibility of the proposed method for real-time industrial deployment. Moreover, Pareto-optimality is verified via half-space analysis, emphasizing the framework’s effectiveness in managing trade-offs among competing objectives.
Keywords: multi-objective optimization; process parameter optimization; reinforcement learning; multi-agent optimization; pinion gear optimization multi-objective optimization; process parameter optimization; reinforcement learning; multi-agent optimization; pinion gear optimization

Share and Cite

MDPI and ACS Style

Paranjape, A.; Quader, N.; Uhlmann, L.; Berkels, B.; Wolfschläger, D.; Schmitt, R.H.; Bergs, T. Reinforcement Learning Agent for Multi-Objective Online Process Parameter Optimization of Manufacturing Processes. Appl. Sci. 2025, 15, 7279. https://doi.org/10.3390/app15137279

AMA Style

Paranjape A, Quader N, Uhlmann L, Berkels B, Wolfschläger D, Schmitt RH, Bergs T. Reinforcement Learning Agent for Multi-Objective Online Process Parameter Optimization of Manufacturing Processes. Applied Sciences. 2025; 15(13):7279. https://doi.org/10.3390/app15137279

Chicago/Turabian Style

Paranjape, Akshay, Nahid Quader, Lars Uhlmann, Benjamin Berkels, Dominik Wolfschläger, Robert H. Schmitt, and Thomas Bergs. 2025. "Reinforcement Learning Agent for Multi-Objective Online Process Parameter Optimization of Manufacturing Processes" Applied Sciences 15, no. 13: 7279. https://doi.org/10.3390/app15137279

APA Style

Paranjape, A., Quader, N., Uhlmann, L., Berkels, B., Wolfschläger, D., Schmitt, R. H., & Bergs, T. (2025). Reinforcement Learning Agent for Multi-Objective Online Process Parameter Optimization of Manufacturing Processes. Applied Sciences, 15(13), 7279. https://doi.org/10.3390/app15137279

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