Ann-based predictive model of geometrical deviations in dry turning of AA7075 (Al-Zn) alloy

This work presents the use of a shallow feedforward artificial neural network (ANN) to develop a prediction model for geometrical deviations in the dry turning of the AA7075 (Al-Zn) alloy. The study focuses on the influence of cutting speed and feed on the arithmetic mean roughness, straightness, and circular runout of cylindrical specimens. The main novelty of this ANN-based model compared to traditional models lies in the simultaneous consideration of geometrical variables at macro and micro scales. The analysis showed that feed was the most influential variable, particularly at higher values, whereas cutting speed had a lesser impact. For all three analysed output variables, the optimal results were achieved by combining low feed and high cutting speed values. The proposed ANN model showed a reasonable adjusted R2 value for all the variables, ranging from 0.87 to 0.97. The ANN performance was compared with other regression models, providing a better fit to the experimental data for all the output variables analysed. Testing of the ANN on additional data not included in the training and validation set confirmed its practical usefulness for predicting geometrical deviations under the studied cutting conditions.
CC BY-NC-ND 4.0This work has received funding from the Ministerio de Ciencia e Innovacíon (Gobierno de España), through the research project “Expert system for improving surface integrity in sustainable machining of light alloys (SPAREMETAL)”, with reference PID2021-125988OB-I00. The authors thank the Universidad de Málaga for their contribution to this work, which has received funding from the “II Plan Propio de Investigacíon, Transferencia y Divulgación Científica” of the Universidad de Málaga, through a grant for a research internship at the School of Engineering Science of the University of Skövde (Sweden). Funding for open access charge: Universidad de Málaga / CBUA.