Response Surface Modelling and Effective Application of Adaptive Neuro-Fuzzy Inference System to Analyze Surface Roughness of Al/Gr/Cp5 MMC Machined using WEDM

2021 ◽  
pp. 1-15
Author(s):  
Mangesh Phate ◽  
Shraddha Toney ◽  
Vikas Phate
Keyword(s):  
Surface Roughness ◽  
Response Surface ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Surface Modelling ◽  
Effective Application ◽  
Neuro Fuzzy ◽  
Response Surface Modelling

Adaptive Neuro-Fuzzy Inference System Modelling of Surface Roughness in High Speed Turning of AISI P 20 Tool Steel

2011 ◽  
Vol 314-316 ◽  
pp. 341-345
Author(s):  
Bo Di Cui
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Tool Steel ◽  
Fuzzy Inference System ◽  
High Speed ◽  
Fuzzy Inference ◽  
System Modelling ◽  
Inference System ◽  
High Speed Turning ◽  
Neuro Fuzzy

Accurate predictive modelling is an essential prerequisite for optimization and control of production in modern manufacturing environments. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the surface roughness in high speed turning of AISI P 20 tool steel. Experiments were designed and performed to collect the training and testing data for the proposed model based on orthogonal array. For decreasing the complexity of the ANFIS structure, principal component analysis (PCA) was used to deal with the experimental data. The comparison between predictions and experimental data showed that the proposed method was both effective and efficient for modelling surface roughness.

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