Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/2039 2018 2018 Alshaab University, Department of Medical Instrumentation Techniques Engineering, Iraq Zaineb Hameed Neamah Alshaab University, Department of Medical Instrumentation Techniques Engineering, Iraq Ahmad Al Al-Talabi Ministry of Electricity, Baghdad, Iraq Asma A. Mohammed Ali Surface roughness (Ra) has a significant influence on the fatigue strength, corrosion resistance, and aesthetic appeal of machine components. Ra is hence a crucial manufacturing process parameter. This study predicts Ra of aluminum alloy Al-7024 after milling. Regression analysis and artificial neural network (ANN) modeling approaches are suggested for predicting Ra values. For better surface roughness, the cutting parameter must be set properly. Spindle speed, feed rate, and depth of cut have been chosen as predictors. Through 31 study cases, regression and ANN were used to examine how these parameters affected Ra. The measurement of surface roughness, together with comprehensive Ra analysis and regression analysis. The findings of this investigation indicate that Ra was predicted by both the regression and ANN models. convergent results from model predictions are obtained. This convergence highlights the promising methodology used in this work to forecast Ra in the milling of Al-7024. The findings demonstrated that, in comparison to the regression model, which had an average variation from the actual values of roughly 1%, The surface roughness was accurately predicted by the ANN model. 2023 2023 37 48 10.54216/FPA.130103 https://www.americaspg.com/articleinfo/3/show/2039