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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

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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.

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Abbreviations

MRR:

Material removal rate

R a :

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

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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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Authors and Affiliations

  1. Department of Mechanical-Mechatronics Engineering, The LNM Institute of Information Technology, Jaipur, Rajasthan, 302017, India

    Deepak Rajendra Unune

  2. Department of Mechanical Engineering, École de Technologie Supérieure (ÉTS), 1100 Notre-Dame Street West, Montréal, QC, H3C 1 K3, Canada

    Mohsen Marani Barzani

  3. Department of Mechanical Engineering, Government College of Engineering Karad, Karad, Maharashtra, 415124, India

    Suhas S. Mohite

  4. Department of Mechanical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, 302017, India

    Harlal Singh Mali

Authors
  1. Deepak Rajendra Unune
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  2. Mohsen Marani Barzani
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  3. Suhas S. Mohite
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  4. Harlal Singh Mali
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Corresponding author

Correspondence to Mohsen Marani Barzani.

Appendix

Appendix

See Table 8.

Table 8 Average surface roughness values along with standard deviation
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Unune, D.R., Marani Barzani, M., Mohite, S.S. et al. 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. Neural Comput & Applic 29, 647–662 (2018). https://doi.org/10.1007/s00521-016-2581-4

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