Otimização robusta no processo de fresamento de topo do aço inoxidável duplex UNS S32205 por meio de aprendizagem de máquina

Abstract

The growing demand for energy efficiency and cost reduction in manufacturing processes has driven the need for optimization in operations such as end milling, especially when machining complex materials like duplex stainless steel UNS S32205. Although this material exhibits excellent mechanical strength and corrosion resistance, it has low machinability due to its tendency to work harden and its low thermal conductivity—factors that compromise process stability and directly affect surface finish quality. In the end milling of this steel, the precise definition of cutting parameters becomes even more challenging due to the presence of noise variables inherent to the process. Given this complexity, the application of machine learning models emerges as a promising approach, provided it is combined with appropriate techniques to construct robust models, that is, models capable of maintaining high predictive performance even in the presence of noise. This thesis aims to develop and validate machine learning models for predicting surface roughness (Ra) in the end milling of duplex stainless steel UNS S32205, simultaneously considering control variables and noise variables. The experiments were conducted based on a central composite design, combining the input variables (cutting speed, feed per tooth, width of cut, and depth of cut) with noise variables (tool flank wear, fluid flow rate, and tool overhang). The study applied the concept of robust parameter design, response surface methodology, and machine learning techniques. The models used included Support Vector Machines (SVM), Decision Tree Regressors (DTR), Random Forests (RF), and Artificial Neural Networks (ANN). Performance evaluation metrics included RMSE, MAE, MSE, and cross-validation using average R2. The ANN models showed the best performance, with the 7-20-14-1 architecture standing out, achieving an R2 of 0.92, RMSE of 0.06, and average R2 of 0.90 in surface roughness prediction. The ANN model was then used as the objective function in an optimization process conducted using the Particle Swarm Optimization (PSO) algorithm, aiming to identify cutting parameters that minimize roughness. Confirmation experiments, performed using a Taguchi L9 array, validated the obtained results, demonstrating that the selected machine learning model was effective in simulating and optimizing the real behavior of the process.