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Prediction model of cutting parameters for turning H-grade high strength steel: a comparative study between regression model and ANFIS

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Document pages: 12 pages

Abstract: H-grade high strength steel is used to manufacture many civil and military products. The procedure for manufacturing these parts has several turning operations. The key factors for manufacturing these parts are accuracy, surface roughness () and material removal rate (MRR). The production line of these parts contains many CNC lathes to obtain good accuracy and repeatability. The manufacturing engineer shall meet the required surface roughness value according to the design drawing of the first test (otherwise these parts will be rejected), and pay attention to the maximum metal removal rate. Rejecting these parts at any processing stage will bring huge problems to any factory, because the processing and raw materials of these parts are very complex expensive. In this paper the artificial neural network was used for predicting the surface roughness for different cutting parameters in CNC turning operations. These parameters were investigated to get the minimum surface roughness. In addition, a mathematical model for surface roughness was obtained from the experimental data using a regression analysis method. The experimental data are then compared with both the regression analysis results and ANFIS (Adaptive Network-based Fuzzy Inference System) estimations.

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