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Method of Selecting a Rational Strategy in Sustainable Friction Drilling

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Języki publikacji
EN
Abstrakty
EN
The presented paper contains a description of a new method of determining the optimum feed rate for a waste-free, energy-efficient drilling process being a friction drilling. Previous experience has shown that the energy required to drill a hole by friction drilling is closely correlated with the drilling cycle time. Based on the authors' own experience, the thesis was put forward that the optimum feed rate during the drilling procedure should be variable but smooth. Additional evaluation criteria for the developed method were the drilling cycle time, the maximum values of the axial force and torque, the maximum values of the feed drive and machine spindle drive load, as well as the energy consumption. The test rig was based on a numerically controlled machine tool, equipped with an axial force and torque sensor, and additional dedicated measuring devices for measuring energy consumption. The results of the study indicate the high competitiveness of the developed approach, compared to the feed rate control strategies in a friction drilling cycle known from the literature. The proposed approach for selecting the longitudinal feed rate can quickly gain a significant number of applications. Studies have shown that optimally planned feed rates can reduce energy intensity by up to 47%.
Twórcy
  • Faculty of Mechanical Engineering and Computer Science, University of Bielsko-Biala, Willowa 2, 43-309 Bielsko-Biala, Poland
  • Faculty of Mechanical Engineering and Computer Science, University of Bielsko-Biala, Willowa 2, 43-309 Bielsko-Biala, Poland
Bibliografia
  • 1. Cai W., Liu F., Zhou X., Xie J. Fine energy consumption allowance of workpieces in the mechanical man- ufacturing industry. Energy 2016; 114(1): 623–633.
  • 2. Chen X., Li C., Tang Y., Li L., Li H. Energy efficient cutting parameter optimization. Front. Mech. Eng. 2021; 16(2): 221–248.
  • 3. Triebe M.J., Mendis G.P., Zhao F., Sutherland J.W. Understanding energy consumption in a machine tool through energy mapping. Proc. CIRP 2018; 69: 259–264.
  • 4. Lenz J., Kotschenreuther J., Westkaemper E. Energy Efficiency in Machine Tool Operation by Online Energy Monitoring Capturing and Analysis. Proc. CIRP 2017; 61: 365–369.
  • 5. Khanna N., Shah P., Sarikaya M., Pusavec F. Energy consumption and ecological analysis of sustainable and conventional cutting fluid strategies in machining 15–5 PHSS. Sustain. Mater. Technol. 2022; 32: 1–13.
  • 6. Composeco-Negrete C. Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA. J. Clean. Prod. 2013; 53: 195-203.
  • 7. Jia S., Wang S., Zhang N., et al. Multi-objective parameter optimization of CNC plane milling for sustainable manufacturing. Environ. Sci. Pollut. Res. 2022.
  • 8. Liu P., Liu F., Qiu H. A novel approach for acquiring the real-time energy efficiency of machine tools. Energy 2017; 121: 524–532.
  • 9. Pawanr S., Garg G.K., Routroy S. Development of a Transient Energy Prediction Model for Machine Tools. Proc. CIRP 2021; 98: 678–683.
  • 10. Bednarek J., Rzadkowski J. Analysis of hollow shape semi rigid joints made by use of thermal drilling technology. IOP Conf. Ser.: Mater. Sci. Eng. 2019; 661: 012082.
  • 11. Kumar R., Rajesh Jesudoss Hynes N. Thermal drilling processing on sheet metals: A review. Int. J. Lightweight Mater. Manuf. 2019; 2(3): 193–205.
  • 12. Pereira O., Urbikain G., Rodriguez A., et al. Process performance and life cycle assessment of friction drilling on dual-phase steel. J. Clean. Prod. 2019; 213: 1147–1156.
  • 13. Szwałek K., Nadolny K. Characteristics of tool used in the friction drilling method. J. Mech. Energy Eng. 2018; 2(2): 109–114.
  • 14. El-Bahloul S.A., El-Shourbagy H.E., El-Bahloul A.M., El-Midany T.T. Experimental and thermo-mechanical modeling optimization of thermal friction drilling for AISI 304 stainless steel. CIRP J. Manuf. Sci. Technol., 2018; 20: 84–92.
  • 15. Dehghan S., Ismail M.I.S., Ariffin M.K.A., Baharudin B.T.H.T. Friction drilling of difficult-to-machine materials: Workpiece microstructural alterations and tool wear. Metals 2019; 9(9): 945.
  • 16. Li H., Wu J., Chen L., Zhang C., Li Z. An improved drilling force model in friction drilling AISI 321. IOP Conf. Series: J. of Phys. 2018; 1074: 012147.
  • 17. Moorthy N., Kanish T.C. The impact of proces parameters on surface roughness and bushing in friction drilling, IOP SciNotes 2020; 1(3): 034402.
  • 18. Demir Z. An experimental investigation of the effect of depth and diameter of pre-drilling on friction drilling of A7075-T651 alloy. J. Sustain. Construct. Mater. Technol. 2016; 1(2): 46–56.
  • 19. Eliseev A., Kolubaev E. Friction drilling: a review. Int. J. Adv. Manuf. Technol. 2021; 116: 1391–1409.
  • 20. Alphonse M., Bupesh Raja V.K., Gupta M. Investigation on tribological behavior during friction drilling process - a review. Tribol. Ind., 2020; 42(2): 200–213.
  • 21. Rao K.H., Gopichand A., Kumar N.P., Jitendra K.Optimization on machining parameters in friction drilling process. Int. J. Mech. Eng. Technol. 2017; 8(4): 242–254.
  • 22. Patil S.S., Bembrekar V. Optimization and thermal analysis of friction drilling on aluminium and mild steel by using tungsten carbide tool. Int. Res. J. Engin. Technol. 2016; 3(12): 1468–1474.
  • 23. Potdar A., Sapkal S. Optimization of friction drilling process by response surface methodology. In: R. Venkata Rao, J. Taler (eds), Advanced engineering optimization through intelligent techniques. Adv. Intell. Syst. Comput. 2019; 949: 351–359.
  • 24. Hynes N.R., Kumar R., Sujana, J.A. Optimum bushing length in thermal drilling of galvanized steel using artificial neural network coupled with genetic algorithm. Mater. Technol. 2017; 51(5): 813–822.
  • 25. Rajesh Jesudoss Hynes N., Kumar R. Process optimization for maximizing bushing length in thermal drilling using integrated ANN-SA approach. J. Braz. Soc. Mech. Sci. Eng. 2017; 39: 5097–5108.
  • 26. Kumar R., Rajesh Jesudoss Hynes N. Prediction and optimization of surface roughness in thermal drilling using integrated ANFIS and GA approach. Int. J. Eng. Sci. Technol. 2019; 23(1): 30–41.
  • 27. Kaya M.T., Aktas A., Beylergil B., Akyildiz H.K. An experimental study of friction drilling of ST12 steel. Trans. Can. Soc. Mech. Eng. 2014; 38: 319–329.
  • 28. Stryczek R., Błaszczak P. Optimal feed rate control strategies for friction drilling. Facta Universitatis. Ser.: Mech. Eng. 2020; 18(4): 545–564.
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  • 30. Jia S., Yuan Q., Cai W., et al. Establishment of animproved material-drilling power model to suport energy management of drilling processes. Energies 2018; 11(8): 2013.
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Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f9ed6e6c-a489-4dbc-876e-2e1d14eebbd8
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