## 1 Introduction
[4] detailed several models for chip formation, which can be used to predict cutting forces, temperatures, stress and strain. These models are based on insert geometries and cutting parameters. The approach [12] proposes a system software to optimize cutting parameters based on genetic algorithms. This model defines an objective function, which is based on theoretical models that relate fundamental variables in the machining operation. [PERSON] et al. [5] showed an optimization of cutting parameters for the turning process. This research defines the surface roughness as an objective function for turning machining of EN 8 steel. A genetic algorithm is also applied in this approach. This model can be considered to be a hybrid model. Although the study by [PERSON] et al. [9] predicts annual power load consumption, the approach also shows an interesting hybrid model based on regression neural networks and the fruit fly optimization algorithm. This research presents a significant improvement in accuracy compared with previous approaches without neural network algorithms. Other models also show important achievements after applying training-based models that use the neural network algorithm. For instance, [PERSON] et al. [15] used the weld bead geometry prediction; [PERSON] et al. [16] predicted the tensile behavior of tailor weld blanks; and [PERSON] and [PERSON] [17] presented a model for surface roughness and tool wear for turning operations. Additionally, [PERSON] et al. [18] created an artificial neural network to predict the mechanical properties of titanium alloys as a function of the alloy composition. The research by [PERSON] et al. [19] poses a singular model for intelligent stock trading decision support systems. This model captures the stock expert's knowledge by a genetic-algorithm-based fuzzy neural network model. Other important achievements for the optimization of cutting parameters have been proposed using only genetic algorithms. Although these approaches show dependency on the working conditions and the workpiece and tools, they can acquire models that can reach the optimal result with high efficiency. In the paper by [PERSON] et al. [20], a multi-objective optimization of cutting parameters in turning operations is presented. This approach entails the use of an objective function based on power consumption, cutting forces and surface roughness. It also presents a qualification of chromosome population, which is inherent to genetic algorithms, based on feasible individuals. [PERSON] and [PERSON] [21] presents an approach for cutting parameters in turning operations. This paper also shows an experimental test for validating the model. [PERSON] et al. [22] proposes a model to predict surface roughness. This model uses the response surface methodology and a genetic algorithm to converge to an optimal solution. [PERSON] and [PERSON] [23] showed an approach for optimization of cutting parameters using the Taguchi method and the analysis of variance (ANOVA) for a database obtained by testing surveys. [PERSON] et al. [24] presents a similar approach using the Taguchi method and the ANOVA analysis but for optimizing surface roughness. Unlike other research approaches, our research considers the insert information from a tool supplier to obtain neural network models of insert before determining the optimal cutting parameters. This approach allows the selection of a tool-insert and the inference of the corresponding cutting parameters by simultaneously considering tool specifications and working conditions. To do so, the research is based on a considerable amount of information defined by the tool supplier. The proposed model is an integrated optimization system, which selects a suitable tool-insert and suggests optimal cutting parameters based on certain working conditions and a fitness function optimization, respectively.