## 1 Introduction
This approach models the relationships between the geometrical and mechanical features of a tool-insert and the working conditions, thus introducing a novel approach to the modeling of cutting parameters for a turning tool. The output of the proposed approach is a set of recommended cutting parameters for an optimally selected cutting tool. This approach considers the entire information defined by the tool supplier and its intrinsic relationship with the working material in a turning operation. Thus, this makes more assertive recommendations for the cutting parameters. The objective of this research is to obtain a model for cutting insert selections and cutting parameter optimization. This model must be constrained by working conditions and evaluated by an objective function. The objective function of this approach is defined as a combination of the lowest power consumption, the shortest machining time and surface roughness within a certain range. However, this function must be customizable under external conditions. Furthermore, the cutting insert selection must be based on commercially available tools. Since this research proposes a model for cutting insert selections based on commercially available tools, it requires a database from a tool supplier. The chosen tool supplier was Sandvik Coromant and the selected tool-insert model for building the datasets was CoroTurn@107. The information about the recommended cutting parameters and the insert feature description was referenced from the official Sandvik Coromant website [25]. The models used on this approach are two artificial neural networks. The first neural network model defines the cutting parameter feed rate as a function of macro-geometrical features and recommended cutting depths. The second model defines the cutting speed as a function of material cutting specifications, working conditions and the feed rate cutting parameter. To find optimal cutting parameters and a suitable cutting insert, a genetic algorithm optimization is proposed based on working conditions. This algorithm is defined by a heuristic search of insert features and cutting parameters, which are evaluated by the neural network models. This heuristic search is set up under a defined objective function, which is a combination of the lowest power consumption, the shortest machining time and an acceptable surface roughness. Due to the heuristic search of the genetic algorithm, the result might be a non-existent tool-insert. Thus, the last stage of this approach is to evaluate a Euclidean distance to find the closest existent tool-insert in the commercial database based on a predefined threshold. The structure of this paper is as follows. Section 2 aims to introduce the main features that define a tool-insert and the relations of the cutting parameters with geometrical features and the working conditions. This section also defines the database based on commercial data from a tool supplier. The section ends with the proposed dataset for this research. Section 3 explains the procedure used to obtain neural network models for this research. Furthermore, this section introduces data preparation and error validation. Section 4 details the mechanism behind the genetic algorithm implementation. Some concepts, such as individual chromosomes, encoding and decoding procedures and fitness function, are introduced in this section. Section 5 aims to explain how to use this model for practical applications in more detail. Examples of applications shown in this section include light roughing machining, heavy roughing machining and finishing operations. Section 6 summarizes this paper.