## 1 Introduction  
The aluminum alloys have gained the prime significance in diverse engineering applications. Machining characteristics of these alloys depend upon the appropriate cutting parameters, such as the cutting speed, cutting feed, cutting tool geometry, clamping scheme, and the tool wear rate. Because of the costly experimentation, assessment of the appropriate cutting parameters implies the application of numerical cutting simulations. However, the execution of numerical cutting simulations implicatesa detailed understanding of different engineering processes that involve during the machining cutting action. Likewise, the material damage at a high strain rate makes it difficult to comprehend the tool-chip interaction [1; 2]. Generally, the crack and material damage behavior are elaborated with the Johson-Cook (JC) plasticity material model and damage evolution law [3] which undertakes the large deformations, high strain rates, and temperature-dependent viscoplasticity. In the turning operation, cutting tool geometry plays a dynamic role in influential cutting force and the chip temperature. Recently, different research efforts have been conducted to describe the behavior of material cutting during the turning operation. [PERSON] [4] described the effect of tool rake angle on the chip morphology by using an elastic-plastic model. [PERSON] [5] studied the interaction of cutting speed and the chip morphology with high-speed cameras. [PERSON] et al. [6] investigated the effect of tool rake angle on material residual stresses during the turning process. [PERSON] et al. [7] also described the behavior of material residual stresses in the orthogonal cutting process. [PERSON] et al. [8] and [PERSON] et al. [9] worked on the experimental investigations of cutting forces. [PERSON] et al. [10] conducted some experiments to study the cutting forces and tool-tip temperature at different feeds and rake angles. [PERSON] et al. [11] performed the numerical cutting simulations to explain the influence of contact friction and tool rake angles upon the thermo-mechanical properties. [PERSON] et al. [12] studied the effect of shear stresses on chip morphology. [PERSON] et al. [13] carried out a detailed study on surface finish during the turning operation. [PERSON] et al. [14] performed a detailed study to identify the cutting temperature by estimating the sensible heat energy. Many researchers have employed the heuristic optimization techniques to identify the optimal cutting parameters of aluminum alloys. [PERSON] and [PERSON] [15] used the genetic algorithm to identify the optimized turning parameters. [PERSON] et al. [16] identified the optimized turning parameters by applying the particle swarm optimization. [PERSON] and [PERSON] [17] estimated the optimal cutting parameters of a cylindrical turning process by using the artificial bee colony algorithm. [PERSON] et al. [18] studied the optimized turning parameters by integrating the genetic algorithm with support vector regression and the artificial neural networks. [PERSON] et al. [15] investigated the tool wear and its pattern by applying the DNA-based computing. [PERSON] et al. [19] worked on the surface roughness modeling of finish face milling under dry cutting conditions by applying the artificial neural networks. Researchers have also identified the efficient use of ANN models in other metal machining processes; for example, the prediction of cutting forces, machining vibrations, tool wear rate [20], milling and drilling [21; 22; 23], the skin pass rolling [24], etc. Similarly, ANNs have been applied effectively to determine the machining surface roughness [25; 26; 27] and the optimal cutting conditions [28; 29]. In this study, the ANN is considered because of the complex nonlinear optimization problem. As compared to the traditional approaches, the ANN can learn the solutions and predict the complex interactions of the input and output data with significant accuracy [30].