Cut Smarter, Move Less: A Hybrid Strategy for Laser Cutting Path Optimization

Efficient extraction of small 2D components from larger sheets is a persistent challenge in manufacturing, classified under Cutting and Packing (C&P) problems. This study focuses on the Laser Cutting Path Planning Problem (LCPP), which seeks an optimal sequence of cutting and gliding head movements to minimize total processing time. In real-world scenarios, cutting and gliding speeds vary by machine, affecting path planning—a factor often overlooked in simplified models. This work proposes a hybrid approach to optimize cutting paths in layouts with multiple irregular parts. It integrates spatial data structures (QuadTree), efficient search algorithms (KD-Tree), and minimum spanning tree (MST) heuristics to reduce displacements and total distance traveled, assuming gliding is faster than cutting. The goal is to decrease operation time on machines such as CNC and laser cutters. Experimental results using synthetic and real-world layouts demonstrate the method’s effectiveness, confirming its practical applicability and computational efficiency in large-scale industrial scenarios.