Leveraging metaheuristic algorithms with improved hybrid prediction model framework for enhancing surface roughness optimization in CNC turning AISI 316

Utilization of prediction and optimization techniques in machining operations assists with the decision-making required for machining parameter selection, directly impacting process outcomes including the surface roughness of the workpiece. Often these methods exclude the consideration of tool wear and critical information contained within sensor data collected during the cutting process. This study enhances the application of a hybrid physics-based and machine learning predictive framework for Ra that incorporates tool wear information via a focused investigation of computer numerical control (CNC) turning AISI 316, followed by a comparative analysis of metaheuristic optimizers to determine the optimal machining parameters. The experimental results include an analysis on the influence of the machining parameters, flank wear, and total cutting distance of the tool on the surface roughness. The proposed prediction model was improved for AISI 316 from a previous study conducted by the authors. The model achieved a root-mean-square error (RMSE) of 0.108 μm and testing results indicated that 87% of predictions fell within limits set by the ASME B46.1-2019 standard. Genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) are compared using the modified prediction model as the objective function. Despite GA producing the lowest minimum Ra for constrained and unconstrained testing cases, SA generated the highest accuracy during validation testing, achieving an error of 4.54% for a constrained scenario. The outcomes of this study strengthen the hybrid prediction framework proposed by the authors and reinforce the value the optimization process provides to machining operators in the manufacturing industry.