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Drilling is an essential part of processes used in subtractive manufacturing for achieving the desired products. Its massive usage requires that any parameter optimization in the process promotes the efficiency and the greener machining. Cutting tool technology is considered one of the most significant impact factors on the sustainability of machining processes and systems. Therefore, the best choice of cutting tools and parameters for the quality of the products and the tool life criterion are both critical for the sustainability metrics of a drilling process. The correct usage of the cutting tool can affect the reduction of needed resources and energy for the production of a new one. Aluminium alloy 7075 (Al7075) is suitable for a variety of specific applications in the aerospace and chemical industries because of its excellent mechanical properties. Its strength, which is comparable to many steels and its excellent corrosion resistance are the main reasons that researchers focus on it [1] to [3]. The chemical composition of Al7075 is given in Table 1. The development of an appropriate cutting factor analytical model is challenging. Nevertheless, there is much research developed about the suitability of different empirical models to predict different factors during several cutting processes. A number of methods, such as artificial neural networks (ANN) and response surface methodology (RSM), have been developed for modelling many manufacturing parameters. The successful application of ANN for prediction and modelling purposes in several science and engineering domains is substantial. ANN is utilized for modelling and prediction purposes due to its advantages in nonlinear response and when time variability occurs. It covers the difficulty in inferring input/output mapping [4] and [5] and the search algorithms for optimization, based on genetic and evolution principles [6] to [8]. RSM is a reliable statistical tool for the mathematical modelling of engineering systems and for the optimization of different manufacturing processes. Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies Efkolidis, N. – García Hernández, C. – Huertas Talón, J.L. – Kyratsis, P. Nikolaos Efkolidis1,* – César García Hernández1 – José Luis Huertas Talón1 – Panagiotis Kyratsis2 1University of Zaragoza, Department of Design and Manufacturing Engineering, Spain 2Western Macedonia University of Applied Sciences, Department of Mechanical Engineering & Industrial Design, Greece