Otimização multiobjetivo evolutiva do torneamento interno de tubos de Peek por meio de modelos de reforço extremo de gradiente

Abstract

Turning is one of the most widely used manufacturing processes in the industry. Its extensive application means that turning processes are increasingly focused on producing of high-quality parts, aiming to associate efficiency, precision, and productivity. The challenges of achieving high-precision surface finishes are even greater when internal turning is applied to modern materials such as polyetheretherketone (PEEK). To achieve the best process conditions, predictive models must be estimated, and optimization must be conducted. This work presents a statistical learning approach for modeling and optimizing the internal turning process in PEEK tubes. Average roughness and roundness of the hole were measured to quantify the hole quality. The cutting force, considered an important indicator of machinability, was also measured. Cutting speed, feed rate, and fixture position were considered as input parameters. For modeling, a learning procedure was proposed, considering polynomial response surface regression, generalized additive methods, treebased methods, support vector regression and extreme gradient boosting. Cross-validation was used for learning and model selection, including k-fold and bootstrap approaches. The results indicated that the extreme gradient boosting model was the best for all predictors. For Ra the final prediction metrics results were RMSE = 0.1395, MAE = 0.1126, and R2 =1.0000, for Fc, RMSE =1.8609, MAE =0.9311, and R2 =0.9280, and for Ront, RMSE = 21.3084, MAE = 17.8053, and R2 = 0.6562. Multi-objective evolutionary optimization was performed, considering the extreme gradient boosting models for average roughness, roundness, and cutting force, in addition to the deterministic model of material removal rate. The NSGA-II method was selected considering the hypervolume for the three-objective optimizations. The pseudo-weight approach is used to select high trade-off solutions, facilitating selection in practical production scenarios. For optimization of Ra vs Ront vs MRR, the balance between the three responses was achieved with a higher vc, f = 0.12 mm/v, and fp = 15.14 mm. For optimization of Fc vs Ront vs MRR, the balance between the three responses was achieved with vc = 378.78 m/min, f = 0.10 mm/v, and fp = 13.00 mm. The proposed learning and optimization approach enabled the achievement of the best results for the internal turning process in PEEK and can be applied to other intelligent manufacturing applications.