Predictive Modeling of Surface Roughness in MQL assisted Turning of SiC-Al Alloy Composites using Artificial Neural Network and Adaptive Neuro Fuzzy Inference System
Keywords:
SiC-Al Alloy, Turning, Minimum Quality Lubricant, Surface Roughness, Artificial Neural Network, ANFISAbstract
This research work deals with the analysis of surface roughness in turning SiC-Al Alloy composite through experimental investigation and predictive model formulation using Artificial neural network (ANN) and Adaptive neuro fuzzy inference system (ANFIS). The experiment has been carried out in MQL cutting condition where cutting speed, feed rate and depth of cut have been considered as input parameters to check the desired surface roughness response. Artificial neural network model with four different learning algorithms was used to predict surface roughness. Best performance was attained by 3-6-1 network structure with LM learning algorithm for training dataset and SCG learning algorithm for testing dataset. Higher correlation coefficient value of 0.99932 and 0.99888 prove the adequacy of the predicted ANN model. However, MAPE for ANN predicted value is 5.298 %. ANFIS model has been designed utilizing Gaussian membership function (Gaussmf) with 3 membership function for every input parameter and linear membership function for output parameter. For training both back propagation and hybrid method, 3 membership function were used. Based on the comparison of ANFIS with three types of membership function parameter training, hybrid method provides accurate results as it portrays MAPE value of 0.113542 when compared to the higher MAPE value of back propagation method. Surface viewer plot also suggested the effectiveness of hybrid model as it generates lower value of surface roughness. On the basis of mean absolute percentage error, it can be further concluded that hybrid method can make an impartially accurate prediction of surface roughness in comparison to back propagation method and artificial neural network which does prove the expediency of proposed hybrid model to reduce the surface roughness considerably.
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