Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing electro chemical machining process

doi:10.1007/s11465-013-0277-3

Front Mech Eng

2013, Vol. 8

Issue (4) : 429-442 https://doi.org/10.1007/s11465-013-0277-3

RESEARCH ARTICLE

Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing electro chemical machining process

Reza TEIMOURI¹(

), Hamed SOHRABPOOR²

1. Mechanical Engineering Department, Babol University of Technology, Babol, Iran; 2. Mechanical Engineering Department, Islamic Azad University of Dezful, Dezful, Iran

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Abstract

Electrochemical machining process (ECM) is increasing its importance due to some of the specific advantages which can be exploited during machining operation. The process offers several special privileges such as higher machining rate, better accuracy and control, and wider range of materials that can be machined. Contribution of too many predominate parameters in the process, makes its prediction and selection of optimal values really complex, especially while the process is programmized for machining of hard materials. In the present work in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage and feed rate on material removal rate (MRR) and surface roughness (SR) the adaptive neuro-fuzzy inference systems (ANFIS) have been used for creation predictive models based on experimental observations. Then the ANFIS 3D surfaces have been plotted for analyzing effects of process parameters on MRR and SR. Finally, the cuckoo optimization algorithm (COA) was used for selection solutions in which the process reaches maximum material removal rate and minimum surface roughness simultaneously. Results indicated that the ANFIS technique has superiority in modeling of MRR and SR with high prediction accuracy. Also, results obtained while applying of COA have been compared with those derived from confirmatory experiments which validate the applicability and suitability of the proposed techniques in enhancing the performance of ECM process.

Keywords electrochemical machining process (ECM) modeling adaptive neuro-fuzzy inference system (ANFIS) optimization cuckoo optimization algorithm (COA)

Corresponding Author(s): TEIMOURI Reza,Email:reza_teimoori@yahoo.com

Issue Date: 05 December 2013

Cite this article:

Reza TEIMOURI,Hamed SOHRABPOOR. Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing electro chemical machining process[J]. Front Mech Eng, 2013, 8(4): 429-442.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-013-0277-3
https://academic.hep.com.cn/fme/EN/Y2013/V8/I4/429

Fig.1 Basic structure of an ANFIS model []

Fig.2 Structure of developed ANFIS model for predicting (a) MRR and (b) SR

Fig.3 A flow chart of cuckoo optimization algorithm

Fig.4 The ECM machine that used in this work

Tab.1 main properties of workpiece

Tab.2 process factors and their levels

Tab.3 design matrix and experimental results

Tab.4 Values of RMSE in testing of MRR and SR for various types of membership function under 2-2-2-2 ANFIS structure

Fig.5 Comparison between measured and predicted values of testing data for (a) MRR and (b) SR

Fig.6 Comparison between measured and predicted values of all data for (a) MRR and (b) SR

Fig.7 Obtained surfaces of MRR through ANFIS model versus (a) electrolyte flow rate and electrolyte concentration and (b) feed rate and voltage

Fig.8 Obtained surfaces of SR through ANFIS model versus (a) electrolyte flow rate and electrolyte concentration and (b) feed rate and voltage

Tab.5 Setup parameters of COA for implementation

Tab.6 Obtained optimal results based on various weight factors

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