Prediction of cutting and feed forces for conventional milling process using adaptive neuro fuzzy inference system (ANFIS)

Kanhu Charan Nayak, Rajesh Kumar Tripathy, Sudha Rani Panda, Shiba Narayan Sahoo


Due to the extensive use of highly automated machine tools in the industry, the manufacturing requires reliable models for the prediction of output performance of machining processes. The prediction of cutting forces plays an important role in the manufacturing industry. The focus of this paper is to develop a reliable method to predict cutting forces (force in X-direction and force in Z-direction) for milling process during conventional machining of mild steel. This paper implements an adoptive Neuro-fuzzy interface system (ANFIS) to actualize an efficient model for prediction of cutting forces during conventional milling. A set of three input machining parameters like speed, feed and depth of cut, which has a major impact on the cutting forces was chosen as input to represent the machining condition. Our result confirms that ANFIS model with Gaussian member function is a better predictive tool for prediction of milling forces with minimum average test error.


Conventional milling, ANFIS, Hybrid algorithm, Training error, Average test error.

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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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