To address the challenge of cutting double-layer narrow-gap steel plates without damaging the lower plate, we conducted systematic laser-cutting experiments and achieved full penetration of the upper plate without damaging the lower plate. Three cutting outcomes were defined: Unpenetrated, Undamaged, and Damaged. A random forest (RF) classifier was developed to predict the cutting outcomes, achieving 100% precision for the Undamaged class on a test split and an overall accuracy of 96.9%. The feature importance analysis indicates that laser power has an importance score of 0.230, confirming it as the most influential feature in the model. The mean regional variation in undamaged probability was computed to guide the progressive expansion of the search window for parameter optimization. The procedure was conducted with the objective of identifying the most robust parameter combinations over the global parameter space at different cutting speeds. The proposed method provides quantitative decision support for robust undamaged cutting under narrow-gap conditions.
Sun et al. (Wed,) studied this question.