Artificial Neural Network Model for Prediction of Tool Tip Temperature and Analysis

Sakir Tasdemir

Abstract

Technological improvements put computer systems in the center of our life and various scientific disciplines. These can range from controlling a device in our home to public institutions and the industry. One of these disciplines is a sub-area in mechanical engineering called machining is concerned with not only mechanical systems but also computer aided systems. Artificial Neural Networks -an area of artificial intelligence- which is concerned with learning and decision making of computers is a field that scientists are very interested in. In this study, an Artificial Neural Network (ANN) system was designed for predicting the temperature at the tool tip during the machining process. In the metal cutting process, tool tip temperature is one of the conditions that must be identified, analyzed and monitored along with the main cutting force. For this purpose, an ANN model was developed to determine The Tool Tip Temperature (TTT) in the turning process. In the designed ANN model, parameters consisting of three inputs and one output were used. The three input variables were rake angle (γ-o), approaching angle (c-o), feedrate (f-mm/rev) respectively. The output parameter was the TTT (T-0C). The most appropriate model was determined according to Mean Squared Error (MSE) ration. In the test phase of the ANN, the smallest MSE was obtained with the model formed as 3-5-1. In this network settings, calculations were MSE=0.00144, R2=0.9956 (absolute fraction of variance) in the training phase and MSE=0.00231, R2=0.9954 in the test phase. The results show that the designed ANN model can be used for predicting and analyzing tool tip temperature.

Keywords

Artificial Neural Network, Prediction Model, Tool Tip Temperature, Turning

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Submitted: 2018-03-09 14:23:08
Published: 2018-03-29 15:53:52
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