Steel Cutting Path Planning for Multi-Contour Bridging Parts with Improved Genetic Algorithm

Steel plates, as one of the most commonly used construction materials, hold a pivotal role in various industries, including construction, manufacturing, and automotive. Consequently, effective cutting of steel plates is essential. Designing an optimal cutting path based on layout requirements to minimize idle cutting time is crucial for the profitability of steel processing enterprises. This paper addresses the problem of optimal cutting path planning for steel plates. Utilizing a generalized traveling salesman model and a feature point sampling strategy, we propose a more versatile modeling approach for cutting multi-part, multi-contour configurations with 'bridge' structures. To solve this problem, we present an improved genetic algorithm (IGA) that addresses the ordering constraints of multiple contours. Additionally, we incorporate principles from swarm intelligence to enhance the algorithm's global optimization capabilities, thereby avoiding local optima and achieving a globally optimized cutting path. Finally, experiments validate the advantages of our modeling and solving method for the multi-part, multi-contour cutting problem with 'bridge' structures, successfully yielding the shortest cutting path for steel plates.