ADAPTIVE CONTROL OF TOOL WEAR BY GREY WOLF OPTIMIZATION AND NEURAL CONTROLLER IN DRILLING, 53-58.

J. Susai Mary, M.A. Sai Balaji, A. Arockia Selvakumar, and D. Dinakaran

References

  1. [1] F. Cus, U. Zuperl, E. Kiker, and M. MIlfelner, Adaptivecontroller design for federate maximization of machining pro-cess, Journal of Achievements in Materials and ManufacturingEngineering, 17(1–2), 2006, 237–240.
  2. [2] Y. Korem, Adaptive control systems for machining, Manufac-turing Review, 2(1), 1989, 6–15.
  3. [3] R. Teti, K. Jemielniak, G. O’Donnell, and D. Dornfeld, Ad-vanced monitoring of machining operations, CIRP Annals –Manufacturing Technology, 59, 2010, 717–739.
  4. [4] D. Dinakaran, S. Sampathkumar, and N. Sivashanmugam, Anexperimental investigation on monitoring of crater wear inturning using ultrasonic technique, International Journal ofMachine Tools and Manufacture, 49(15), 2009, 1234–1237.
  5. [5] A. Gajate, R. Haber, R. del Toro, P. Vega, and A. Bustillo,Tool wear monitoring using neuro-fuzzy techniques: a com-parative study in a turning process, Journal of IntelligentManufacturing, 23, 2012, 869–882.
  6. [6] J. Susai Mary, M.A. Sai Balaji, A. Krishnakumari, R.S.Nakandhrakumar, and D. Dinakaran, Real time monitoring ofdrill runout using least square support vector machine classifier,Measurement, 146, 2019, 24–34.
  7. [7] J. Susai Mary, M.A. Sai Balaji, and D. Dinakaran, Predictionand geometric adaptive control of surface roughness in drillingprocess, FME Transactions, 47(3), 2019, 424–429.
  8. [8] M.S. Charoo, M.F. Wani, and G.A. Harmain, Linear regressionmodel to predict tool wear on the machining of GFRP, Ecotrib2009 – 2nd European Conf. On Tribology, Pisa, Italy, 2010.
  9. [9] J.C. Chen and J.C. Chen, A multiple-regression model formonitoring tool wear with a dynamometer in milling operations,The Journal of Technology Studies, (4), 2013, 71–77.
  10. [10] J.V. Abellan, F. Romero, H.R. Siller, A. Estruch, and C.Vila, Adaptive control optimization of cutting parameters forhigh quality machining operations based on neural networksand search algorithms, Advances in Robotics, Automation andControl (London: InTech Open, 2008), 472–492.
  11. [11] X.-M. You, S. Liu, and C. Zhang, An improved ant colonysystem algorithm for robot path planning and performanceanalysis, International Journal of Robotics and Automation,33(5), 2018, 527–533.
  12. [12] D. Petkovic and M. Radovanovic, Using genetic algorithmsfor optimisation of turning machining process, Journal ofEngineering Studies and Research, 19, 2013, 47–55.
  13. [13] J. Susai Mary, U. Sabura Banu, D. Dinakaran, and RSNakandhrakumar, Adaptive control by multi-objective opti-misation for drilling process with fuzzy inference system and57neural predictive controller The Journal of The British Instituteof Non-Destructive Testing, 59(1) 2017, 38–44.
  14. [14] J. Susai Mary, U. Sabura Banu, and D. Dinakaran, Interna-tional conference on Robotics Automation Control and Embed-ded systems, Hindustan Institute of Technology and Science,Chennai, 18–20 Feb. 2015.
  15. [15] U. Jayakrishnan, I. Sanghrajka, S. Manikandakumar, T.Muthuramalingam, M. Goldberg, and G. Littlefair, Optimi-sation of multiple response characteristics on end milling ofaluminium alloy using Taguchi-Grey relational approach, Mea-surement, 124, 2018, 291–298.
  16. [16] S. Mirjalili, S.M. Mirjalili, and A. Lewis, Grey wolf optimizer,Advances in Engineering Software, 69, 2014, 46–61.
  17. [17] M. Sekulic, V. Pejic, M. Brezocnik, M. Gostimirovic, and M.Hadzistevic, Prediction of surface roughness in the ball-endmilling process using response surface methodology, geneticalgorithms, and grey wolf optimizer algorithm, Advances inProduction Engineering & Management, 13(1), 2018, 18–30.
  18. [18] G.V.A Kumar and K.L Narasimhamu, Multi-objective op-timization in WEDM of Inconel 750 alloy: Application ofTOPSIS embedded grey wolf optimizer, Advanced EngineeringOptimization through Intelligent Techniques, Advances in In-telligent Systems and Computing, Vol. 949 (Berlin: Springer,2019), 231–240.
  19. [19] X. Dong, Z. Jian-qu, and W. Feng, Fuzzy PID control to feedservo system of CNC machine tool, Procedia Engineering, 29,2012, 2853–2858.
  20. [20] C. Hao, Y. Wang, H. Wang, and Z. Zhou, Model-free adap-tive control for time-varying trajectory tracking of non-linearsystems, International Journal of Robotics and Automation,34(1), 2019, 71–77.

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