A comparative study of non-liner regression, artificial neural network and neuro-fuzzy for the prediction of thrust force in drilling of bagasse fiber-reinforced vinyl ester composite sheet
نویسندگان
چکیده
The aim of this work is to study the applications of soft computing technique on the machinability of bagasse fiber-reinforced vinyl ester composites. The prediction accuracy of regression, artificial neural networks, and neuro-fuzzy methods on the response variables during machining of bagasse fiber-reinforced vinyl ester composite sheets.. In experimental part, a total of 27 experiments was conducted by varying the drill diameter, feed rate and cutting speed and the corresponding values of thrust force was measured. After measuring thrust force, the evaluations of data were performed by using regression, artificial neural networks, and neurofuzzy methods. Average absolute percentage error of regression, artificial neural networks, and neurofuzzy methods were compared. Neuro-fuzzy model is a more powerful tool than the ANN and regression
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