scholarly journals Modeling the effect of variable work piece hardness on surface roughness in an end milling using multiple regression and adaptive Neuro fuzzy inference system

2014 ◽  
Vol 5 (2) ◽  
pp. 265-272 ◽  
Author(s):  
Purushottam S. Desale ◽  
Ramchandra S. Jahagirdar
Keyword(s):  
Surface Roughness ◽  
Multiple Regression ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
End Milling ◽  
Work Piece ◽  
Inference System ◽  
Neuro Fuzzy

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.

Application of Multiple Regression and Adaptive Neuro-Fuzzy Inference System for Prediction of Surface Roughness in EDM

2012 ◽  
Author(s):  
S. S. Baraskar ◽  
S. S. Banwait
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Multiple Regression ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Input Output ◽  
Output Data ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy

A manufacturing system is oriented towards higher production rate, better quality and reduced cost and time to make a product. Surface roughness is an index parameter for determining the quality of a machined product and is influenced by various input process parameters. Surface roughness prediction in Electrical Discharge Machine (EDM) is being attempted with many methodologies, yet there is a need to develop robust, autonomous and accurate predictive system. This work proposes the application of hybrid intelligent technique, multiple regression and adaptive neuro-fuzzy inference system (ANFIS) for prediction of surface roughness in EDM. An experimental data set is obtained with current, pulse-on time and pulse-off time as input parameters and surface roughness as output parameter. Central composite rotatable design was used to plan the experiments. Multiple regression model is developed using the experimental data, to generate additional input-output data set. The input-output data set is used for training and validation of the proposed technique. After validation, data are forwarded for prediction of surface roughness. The proposed hybrid model for the prediction of surface roughness has very good agreement with the experimental results.

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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