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Neural Computing and Applications

, Volume 29, Issue 9, pp 647–662 | Cite as

Fuzzy logic-based model for predicting material removal rate and average surface roughness of machined Nimonic 80A using abrasive-mixed electro-discharge diamond surface grinding

  • Deepak Rajendra Unune
  • Mohsen Marani Barzani
  • Suhas S. Mohite
  • Harlal Singh Mali
Original Article

Abstract

In this paper, a fuzzy logic artificial intelligence technique is delineate to predict the material removal rate (MRR) and average surface roughness (R a) during abrasive-mixed electro-discharge diamond surface grinding (AMEDDSG) of Nimonic 80A. Though, Nimonic 80A superalloy is extensively used in aerospace and automotive industries due to its high corrosion, fracture toughness, oxidation, and temperature resistance characteristics, being a difficult-to-cut material, its machining is a challenging job. The hybrid machining processes like AMEDDSG can be competently used for machining of Nimonic 80A. The face-centered central composite design is used consummate the experiments and then experimental data are used to establish fuzzy logic Mamdani model to predict the MRR and R a with respect to changes in the input process parameters viz. wheel RPM, abrasive concentration, pulse current and pulse-on-time. The results of confirmation experiments reveal an agreement between the fuzzy model and experimental results with 93.89 % accuracy implying that the established fuzzy logic model can be precisely used for predicting the performance of the AMEDDSG process. An increase in wheel RPM, pulse current, and pulse-on-time from their low level to high level contributes to increased MRR by 83.89, 71.01, 17.02 %, respectively. Also, an increase in wheel RPM contributes to reduced R a values by 5.96 %. Abrasive concentration increase from 0 to 4 g/L improves MRR by 24.03 %. The 17.10 % improvement in surface finish is achieved by increasing abrasive concentration from 0 to 8 g/L.

Keywords

Fuzzy Logic Nimonic 80A Abrasive Electro-discharge Grinding 

Abbreviations

MRR

Material removal rate

Ra

Average surface roughness

AMEDDSG

Abrasive-mixed electro-discharge diamond surface grinding

HMPs

Hybrid machining processes

EDG

Electro-discharge grinding

EDDG

Electro-discharge diamond grinding

ECDG

Electrochemical discharge grinding

ECDM

Electrochemical discharge machining

EDDCG

Electro-discharge diamond cutoff grinding

EDDFG

Electro-discharge diamond face grinding

EDDSG

Electro-discharge diamond surface grinding

EDM

Electro-discharge machining

HSS

High speed steel

WC–Co

Tungsten carbide–cobalt

ANN

Artificial neural network

ANFIS

Adaptive neuro-fuzzy system

DC

Direct current

RSM

Response surface methodology

PMDC

Permanent magnet direct current

SiC

Silicon carbide

MF

Membership function

RMSE

Root-mean-square error

VL

Very low

L

Low

M

Medium

H

High

VH

Very high

E

Excellent

G

Good

A

Average

B

Bad

R

Rough

COA

Centroid of area

IEG

Inter-electrode gap

Notes

Acknowledgments

The authors would like to thank Advanced Manufacturing and Mechatronics laboratory and Materials Research Center at Malaviya National Institute of Technology, Jaipur for providing facilities for conducting this work.

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Deepak Rajendra Unune
    • 1
  • Mohsen Marani Barzani
    • 2
  • Suhas S. Mohite
    • 3
  • Harlal Singh Mali
    • 4
  1. 1.Department of Mechanical-Mechatronics EngineeringThe LNM Institute of Information TechnologyJaipurIndia
  2. 2.Department of Mechanical EngineeringÉcole de Technologie Supérieure (ÉTS)MontréalCanada
  3. 3.Department of Mechanical EngineeringGovernment College of Engineering KaradKaradIndia
  4. 4.Department of Mechanical EngineeringMalaviya National Institute of TechnologyJaipurIndia

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