Automated Surface Integrity Assessment and Adaptive Grinding Parameter Optimization in Magnetic Assisted Finishing (MAF) via Multi-Modal Data Fusion and Reinforcement Learning

**Abstract:** This paper introduces a novel framework for real-time surface integrity assessment and adaptive grinding parameter optimization in Magnetic Assisted Finishing (MAF). Recognizing the limitations of current inspection methods and the need for dynamic process control, we propose a system leveraging multi-modal data fusion (optical microscopy, laser scanning profilometry, and magnetic induction sensing) coupled with a reinforcement learning (RL) agent to optimize grinding parameters for maximized surface quality and reduced processing time. The core innovation lies in the development of a HyperScore metric that comprehensively quantifies surface integrity, enabling effective training of the RL agent and facilitating self-adaptation of the MAF process. This framework promises improved efficiency, reduced waste, and enhanced control in precision surface finishing operations, significantly impacting industries like aerospace, automotive, and microelectronics. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*