## 6 Conclusions  
Neural network models for the selection of cutting inserts and cutting parameters were designed. These models were applied to roughing and finishing operations. This research uses the information from a tool supplier to embed knowledge for the tool-insert selection and optimization system developed in this research. The proposed system is based on artificial neural networks (ANNs) and a genetic algorithm (GA). These represent the modeling and optimization part of this research, respectively. For the modeling, two ANNs were implemented. These ANNs are able to infer the feed rate and cutting velocity parameters. The feed rate model is defined as a function of insert features and a set depth of the cut. The insert features represent the macro-geometries of a tool-insert, which include the cutting length, thickness, nose angle, nose radius, size and grade among others. For the cutting velocity model, the inputs are the material specifications and a set feed rate. The material specifications are defined as inherent features of a working material for turning processes. They include hardness, specific cutting force and ISO material group. For the neural network validation, an error comparison based on density functions was implemented. This approach proposed an alternative solution for the error validation of regression models based on the recommended data by a supplier. To evaluate some architectures of the ANN models, a heuristic search based on GA was used. This approach evaluated the possible architectures and evolved them toward the most feasible one. For the proposed research, a heuristic searching method for a feasible tool-insert based on its characteristics in a certain environment was also introduced. The algorithm used for this search was a GA. The introduced GA optimization searches for an optimal tool-insert, which adapts to a working specification and evaluates an acceptable performance given by a customized objective function. This objective function evaluates the performance under certain working conditions, such as the lowest power consumption, the shortest machining time and an acceptable surface roughness. In this present study, different goal functions referring to different force models or tool life models can be used when designing the fitness function in order to obtain the optimal tool-insert with specific considerations. This research presents a model to simulate knowledge and expertise, which embeds the information from a tool supplier. It returns the most suitable insert-tool as a result, given certain working conditions. This research did not use a lookup table approach in the database as it instead modeled the mechanical relations between the geometrical features of an insert-tool and its recommended cutting parameters. This tool-insert selection and optimization system can embed the data from other models, different tool suppliers, previous machining works and expertise from machine-shop workers. However, because of the inherent discrepancy among different datasets, additional factors, such as input variation and data types, must be considered when using the developed system and approaches. Furthermore, because the developed system successfully represents the complex relationship between the working condition and the cutting parameters, the developed system can be a plug-in for CAD/CAM software or can be integrated with the controller of a CNC machine tool to be an auxiliary function for the automatic selection of the cutting parameters based on the preset working conditions presented in the part program. For this research, a fully connected ANN model was used due to the representation of