Performance Evaluation and Optimization of CNC Toolpath Planning with Machining Error Prediction Using a PO-ELM Approach

In this study, an evaluation of toolpath planning effects on CNC machining performance and error prediction using the PO-ELM algorithm was performed. The research addressed the problem of geometric inaccuracies and inefficiencies in CNC machining by investigating eight toolpath strategies. The primary focus was optimizing machining time, surface quality, and accuracy through predictive modeling. Objectives included examining toolpath effects on machining time and surface finish, studying machine performance, identifying errors, applying PO-ELM for error prediction, and integrating these into a unified workflow. This research uniquely combined toolpath optimization with machine learning-based error prediction, unlike previous studies that focused on isolated parameters. The Morph strategy achieved the shortest machining time (22.4 minutes), while Follow Periphery yielded the best surface finish (0.59 µm). The PO-ELM model demonstrated high prediction accuracy (R²=0.916) with residuals within ±0.002 mm. Vibration levels above 2.0 m/s² correlated with positioning errors exceeding 8 µm. The optimization framework enabled a 25% reduction in machining time and 40% improvement in accuracy compared to conventional strategies. These findings provide a model for enhancing manufacturing efficiency and precision. The integration of toolpath planning with predictive modeling offers significant applications in smart manufacturing and quality control. Recommendations include adopting Morph and Follow Periphery strategies for precision tasks and implementing PO-ELM for proactive error correction. This research contributes to advanced manufacturing by bridging theoretical models with industrial applications.