Adaptive neuro-fuzzy inference system (ANFIS) based surface roughness prediction model for ball end milling operation

2012 ◽  
Vol 4 (3) ◽  
Author(s):  
Shahriar Jahan Hossain
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
End Milling ◽  
Inference System ◽  
Ball End Milling ◽  
Neuro Fuzzy ◽  
Milling Operation ◽  
Roughness Prediction

Surface Roughness Prediction in High Speed End Milling Using Adaptive Neuro-Fuzzy Inference System

2015 ◽  
Vol 1115 ◽  
pp. 122-125
Author(s):  
Muataz Hazza F. Al Hazza ◽  
Amin M.F. Seder ◽  
Erry Y.T. Adesta ◽  
Muhammad Taufik ◽  
Abdul Hadi bin Idris
Keyword(s):  
Surface Roughness ◽  
Fuzzy Inference System ◽  
High Speed ◽  
Fuzzy Inference ◽  
End Milling ◽  
Machining Process ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Roughness Prediction ◽  
Coated Carbide

One of the significant characteristics in machining process is final quality of surface. The best measurement for this quality is the surface roughness. Therefore, estimating the surface roughness before the machining is a serious matter. The aim of this research is to estimate and simulate the average surface roughness (Ra) in high speed end milling. An experimental work was conducted to measure the surface roughness. A set of experimental runs based on box behnken design was conducted to machine carbon steel using coated carbide inserts. Moreover, the Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used as one of the unconventional methods to develop a model that can predict the surface roughness. The adaptive-network-based fuzzy inference system (ANFIS) was found to be capable of high accuracy predictions for surface roughness within the range of the research boundaries.

Thermal Error Prediction of the Spindle Using Adaptive Neuro-Fuzzy Inference System

2015 ◽  
Vol 789-790 ◽  
pp. 263-267
Author(s):  
Yan Lei Li ◽  
Ming Yan Wang ◽  
You Min Hu ◽  
Bo Wu
Keyword(s):  
Prediction Model ◽  
Predictive Model ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Thermal Error ◽  
Temperature Data ◽  
Current Sensor ◽  
Error Prediction ◽  
Inference System ◽  
Neuro Fuzzy

This paper proposes a new method to predict the spindle deformation based on temperature data. The method introduces ANFIS (adaptive neuro-fuzzy inference system). For building the predictive model, we first extract temperature data from sensors in the spindle, and then they are used as the inputs to train ANFIS. To evaluate the performance of the prediction, an experiment is implemented. Three Pt-100 thermal resistances is used to monitor the spindle temperature, and an inductive current sensor is used to obtain the spindle deformation. The experimental results display that our prediction model can better predict the spindle deformation and improve the performance of the spindle.

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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