In this paper, an enhanced random vector functional link network (RVFL) algorithm was employed to predict kerf quality indices during CO₂ laser cutting of polymethylmethacrylate (PMMA) sheets. In the proposed model, the equilibrium optimizer (EO) is used to augment the prediction capability of RVFL via selecting the optimal values of RVFL parameters. The predicting model includes four input variables: gas pressure, sheet thickness, laser power, and cutting speed, and five kerf quality indices: rough zone ratio, widths of up and down heat affected zones, maximum surface roughness, and kerf taper angle. The experiments were designed using Taguchi L18 orthogonal array. The kerf surface contains three main zones: rough, transient, and smooth zones. The results of conventional RVFL as well as modified RVFL-EO algorithms were compared with experimental ones. Seven statistical criteria were used to assess the performance of the proposed algorithms. The results indicate that the RVFL-EO model has the predicting ability to estimate the laser-cutting characteristics of PMMA sheet.

本文采用一种增强的随机矢量功能链接网络（RVFL）算法来预测CO ₂期间的切口质量指数激光切割聚甲基丙烯酸甲酯（PMMA）板材。在提出的模型中，平衡优化器（EO）用于通过选择RVFL参数的最佳值来增强RVFL的预测能力。该预测模型包括四个输入变量：气压，片材厚度，激光功率和切割速度，以及五个切口质量指数：粗糙区域比率，上下热影响区域的宽度，最大表面粗糙度和切口锥角。实验是使用田口L18正交阵列设计的。切口表面包含三个主要区域：粗糙区域，过渡区域和平滑区域。将常规RVF​​L以及改进的RVFL-EO算法的结果与实验结果进行了比较。七个统计标准用于评估所提出算法的性能。