Intelligent Grinding Path Planning for Surface Crack Removal in Special Steel Bars
Abstract
To address the demand for efficient and automated removal of surface crack defects in special steel bars, this paper proposes a grinding path planning method based on hybrid ACO–SA optimization. A geometric model of surface cracks is established, and the key factors influencing grinding efficiency are analyzed. Crack defects are represented as line segments with defined start and end coordinates, which effectively preserves defect continuity and overcomes the limitations of scattered point-based representations. To enhance optimization performance and avoid premature convergence, a task-oriented grinding path planning framework based on a hybrid ant colony optimization (ACO) and Simulated Annealing (SA) strategy is developed, combining the global search capability of ACO with the local optimization strength of SA. Experimental results for 10 m long special steel bars containing 100 randomly distributed cracks demonstrate that the proposed method achieves an average planning time of 9.4 s and a maximum path fluctuation of 324 mm, ensuring both computational efficiency and path stability. Validation using real surface defect data further confirms the effectiveness and practicality of the proposed approach for industrial grinding applications. The proposed method provides an effective solution for automated grinding of surface cracks in special steel bars and offers practical potential for automated defect repair in the steel manufacturing industry.