Artificial neural network based on predictive model and analysis for main cutting force in turning

In manufacturing technology, the foremost issue influencing the usability and cost of products is metal cutting operations. In this operation, it is very difficult to develop a model including all the cutting parameters and tool geometry. Tool geometry that will enable the most suitable cutting conditions will increase the quality of workpiece surface and so the efficiency of the process. The incredible success of Artificial Neural Networks (ANN) in classification and estimation makes it necessary to use this approach in the area. Apart from known methods, ANN, which is an artificial intelligence technique, was used to estimate main cutting force, which is the modeling of a non-linear process. In this study, a novel artificial neural network model was developed in turning operation to determine the main cutting force. The developed ANN has 3 inputs and 1 output. The three input variables were feedrate (f-mm/rev), approaching angle (χ-°), rake angle (γ-°), respectively. The output parameter value was the main cutting force (Fc-N). The results of ANN and experimental data were compared by statistical. The study put forth that accuracy rates obtained from training and test operations can be used in determining the main cutting force in the generated model. © Sila Science.