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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

References

  1. Unune DR, Mali HS (2014) Current status and applications of hybrid micro-machining processes: a review. Proc Inst Mech Eng B J Eng Manuf 229(10):1681–1693. doi:10.1177/0954405414546141

    Article  Google Scholar 

  2. Pervaiz S, Rashid A, Deiab I, Nicolescu M (2014) Influence of tool materials on machinability of titanium- and nickel-based alloys: a review. Mater Manuf Process 29(3):219–252. doi:10.1080/10426914.2014.880460

    Article  Google Scholar 

  3. Shrivastava PK, Dubey AK (2013) Electrical discharge machining-based hybrid machining processes: a review. Proc Inst Mech Eng B J Eng Manuf 228(6):799–825. doi:10.1177/0954405413508939

    Article  Google Scholar 

  4. Koshy P, Jain VK, Lal GK (1996) Mechanism of material removal in electrical discharge diamond grinding. Int J Mach Tool Manuf 36(10):1173–1185

    Article  Google Scholar 

  5. Choudhury SK, Jain VK, Gupta M (1999) Electrical discharge diamond grinding of high speed steel. Mach Sci Technol 3(1):91–105. doi:10.1080/10940349908945685

    Article  Google Scholar 

  6. Yadav SKS, Yadava V, Narayana VL (2008) Experimental study and parameter design of electro-discharge diamond grinding. Int J Adv Manuf Technol 36(1–2):34–42

    Article  Google Scholar 

  7. Singh GK, Yadava V, Kumar R (2010) Diamond face grinding of WC-Co composite with spark assistance: experimental study and parameter optimization. Int J Precis Eng Manuf 11(4):509–518

    Article  Google Scholar 

  8. Shrivastava PK, Dubey AK (2013) Experimental modeling and optimization of electric discharge diamond face grinding of metal matrix composite. Int J Adv Manuf Technol 69(9–12):2471–2480. doi:10.1007/s00170-013-5190-8

    Article  Google Scholar 

  9. Agrawal SS, Yadava V (2013) Modeling and prediction of material removal rate and surface roughness in surface-electrical discharge diamond grinding process of metal matrix composites. Mater Manuf Process 28(4):381–389

    Article  Google Scholar 

  10. Mali HS, Unune DR, Tiwari S (2014) Modelling and prediction of material removal rate in electrical discharge diamond surface grinding process of INCONEL-718. In: Proceedings of 5th AIMTDR 2014. IIT Guwahati, India, pp 822:821–822:828

  11. Unune DR, Mali HS (2015) Artificial neural network-based and response surface methodology-based predictive models for material removal rate and surface roughness during electro-discharge diamond grinding of Inconel 718. Proc Inst Mech Eng B J Eng Manuf. doi:10.1177/0954405415619347

    Google Scholar 

  12. Modi M, Agarwal G (2012) Design, development & experimental investigation of electro-discharge diamond surface grinding of Ti–6Al–4V. Int J Adv Mater Res 418–420:1478–1481. doi:10.4028/www.scientific.net/AMR.418-420.1478

    Google Scholar 

  13. Modi M, Agarwal G (2013) Powder-mixed electro-discharge diamond surface grinding process: modelling, comparative analysis and multi-output optimisation using weighted principal components analysis. Stroj Vestn J Mech Eng 59(12):735–747. doi:10.5545/sv-jme.2013.1146

    Article  Google Scholar 

  14. Unune DR, Singh VP, Mali HS (2015) Experimental investigations of abrasive mixed electro discharge diamond grinding of Nimonic 80A. Mater Manuf Process. doi:10.1080/10426914.2015.1090598

    Google Scholar 

  15. Pradhan MK, Biswas CK (2010) Neuro-fuzzy and neural network-based prediction of various responses in electrical discharge machining of AISI D2 steel. Int J Adv Manuf Technol 50(5–8):591–610. doi:10.1007/s00170-010-2531-8

    Article  Google Scholar 

  16. Zalnezhad E, Sarhan AAD, Hamdi M (2013) A fuzzy logic based model to predict surface hardness of thin film TiN coating on aerospace AL7075-T6 alloy. Int J Adv Manuf Technol 68(1–4):415–423. doi:10.1007/s00170-013-4738-y

    Article  Google Scholar 

  17. Chandrasekaran M, Muralidhar M, Krishna CM, Dixit US (2009) Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol 46(5–8):445–464. doi:10.1007/s00170-009-2104-x

    Google Scholar 

  18. Maher I, Ling LH, Sarhan AAD, Hamdi M (2015) Improve wire EDM performance at different machining parameters - ANFIS modeling. IFAC-PapersOnLine 48(1):105–110

    Article  Google Scholar 

  19. Mahe, I, Eltaib MEH, El-Zahry RM (2006) Surface roughness prediction in end milling using multiple regression and adaptive neuro-fuzzy inference system. In: International conference on mechanical engineering advanced technology for industrial production (MEATIP4). Assiut University, Egypt, pp 614–620

    Google Scholar 

  20. Khanlou HM, Ang BC, Barzani MM, Silakhori M, Talebian S (2015) Prediction and characterization of surface roughness using sandblasting and acid etching process on new non-toxic titanium biomaterial: adaptive-network-based fuzzy inference System. Neural Comput Appl 26(7):1751–1761. doi:10.1007/s00521-015-1833-z

    Article  Google Scholar 

  21. Khanlou HM, Ang BC, Barzani MM (2015) Prediction, modeling and characterization of surface texturing by sulfuric etchant on non-toxic titanium bio-material using artificial neural networks and fuzzy logic systems. Sci Eng Compos Mater. doi:10.1515/secm-2014-0230

    Google Scholar 

  22. Suganthi XH, Natarajan U, Sathiyamurthy S, Chidambaram K (2013) Prediction of quality responses in micro-EDM process using an adaptive neuro-fuzzy inference system (ANFIS) model. Int J Adv Manuf Technol 68(1–4):339–347. doi:10.1007/s00170-013-4731-5

    Article  Google Scholar 

  23. Marani Barzani M, Zalnezhad E, Sarhan AAD, Farahany S, Ramesh S (2015) Fuzzy logic based model for predicting surface roughness of machined Al–Si–Cu–Fe die casting alloy using different additives-turning. Measurement 61:150–161. doi:10.1016/j.measurement.2014.10.003

    Article  Google Scholar 

  24. Khanlou HM, Ang BC, Talebian S, Barzani MM, Silakhori M, Fauzi H (2015) Multi-response analysis in the processing of poly (methyl methacrylate) nano-fibres membrane by electrospinning based on response surface methodology: fibre diameter and bead formation. Measurement 65:193–206. doi:10.1016/j.measurement.2015.01.014

    Article  Google Scholar 

  25. Kumar A, Maheshwari S, Sharma C, Beri N (2011) Analysis of machining characteristics in additive mixed electric discharge machining of nickel-based super alloy Inconel 718. Mater Manuf Process 26(8):1011–1018

    Article  Google Scholar 

  26. Zalnezhad E, Sarhan AAD (2014) A fuzzy logic predictive model for better surface roughness of Ti–TiN coating on AL7075-T6 alloy for longer fretting fatigue life. Measurement 49:256–265. doi:10.1016/j.measurement.2013.11.042

    Article  Google Scholar 

  27. Zimmermann HJ (1985) Fuzzy set theory and its applications. Kluwer Academic Publishers, London

    Book  Google Scholar 

  28. Yadav RN, Yadava V (2015) Application of soft computing techniques for modeling and optimization of slotted-electrical discharge diamond face grinding process. Trans Indian Inst Met. doi:10.1007/s12666-015-0536-6

    Google Scholar 

  29. Yadav SKS, Yadava V (2013) Experimental investigations to study electrical discharge diamond cutoff grinding (EDDCG) machinability of cemented carbide. Mater Manuf Process 28(10):1077–1081

    Article  Google Scholar 

Download references

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
Full size table

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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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