Surface Roughness and Accuracy Optimization in Thin-Walled Components by MDP-Based Reinforcement Learning Technique

Wire Arc Additive Manufacturing (WAAM) involves strongly coupled, nonlinear and time-varying thermal–geometric dynamics, making autonomous control of layer geometry and surface quality a persistent challenge. Existing data-driven and supervised learning approaches primarily address forward prediction and not well suited for real-time, multi-objective decision-making under evolving process conditions. This paper presents a model-based Markov Decision Process (MDP) framework for closed-loop, real-time optimization of WAAM process parameters, explicitly targeting the control of layer-geometry accuracy and reduction of surface roughness and interlayer heat-input variation. The proposed method formulates current and travel speed selection as sequential decision-making actions within an MDP, supported by Generalized Additive Models (GAMs) to predict layer height, width, and roughness for unseen parameter combinations. A novel multi-criteria reward function is designed to jointly penalize geometric deviation, surface undulations along both layer height and width, and abrupt interlayer heat-input changes, enabling an adaptive trade-off that static or single-objective models cannot capture. MDP is employed to compute optimal policies within the inter-pass cooling time, making the approach suitable for real-time deployment without GPU acceleration. Experimental validation on thin-walled SS308L structures demonstrates that the proposed MDP-based control strategy significantly outperforms open-loop deposition. Compared to conventional parameter selection, layer height and width accuracy improved by 2.12% and 10%, respectively, while average surface roughness was reduced by 10.74% along the width and 25.2% along the height. Statistical analysis confirms tighter confidence intervals for both geometry and roughness, indicating improved process stability. The results establish the proposed framework as a computationally efficient, autonomous decision-making controller for WAAM, advancing automation capabilities for high-precision metal additive manufacturing.