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 BarzaniEmail author
  • Suhas S. Mohite
  • Harlal Singh Mali
Original Article


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.


Fuzzy Logic Nimonic 80A Abrasive Electro-discharge Grinding 



Material removal rate


Average surface roughness


Abrasive-mixed electro-discharge diamond surface grinding


Hybrid machining processes


Electro-discharge grinding


Electro-discharge diamond grinding


Electrochemical discharge grinding


Electrochemical discharge machining


Electro-discharge diamond cutoff grinding


Electro-discharge diamond face grinding


Electro-discharge diamond surface grinding


Electro-discharge machining


High speed steel


Tungsten carbide–cobalt


Artificial neural network


Adaptive neuro-fuzzy system


Direct current


Response surface methodology


Permanent magnet direct current


Silicon carbide


Membership function


Root-mean-square error


Very low








Very high












Centroid of area


Inter-electrode gap



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
    Email author
  • 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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