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Predictive Modeling and Control of Clamp Load Loss in Bolted Joints Based on Fractional Calculus

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Advances in Computing and Network Communications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 735))

Abstract

Safety of bolted joints in industrial machinery is of paramount importance. In this paper, fractional calculus-based predictive modeling has been investigated to control clamping force losses in bolted joints under service loads. Clamp load loss occurs in bolted joints due to application and subsequent removal of an externally applied separating service load on a fastener preloaded beyond its elastic limit. In this work, five different model structures were tried for system identification-based predictive modeling of joint clamp load loss. These structures were the first-order integer, second-order integer, first generation CRONE, fractional integral and fractional-order models. These models were validated by statistical parameters such as FIT, \(R^{2}\), mean squared error, mean absolute error, and maximum absolute error. The fractional-order model with three parameters provided most accurate estimate of the system performance. It also took minimum iterations to reach the optimum controller parameter settings. This model was controlled using PID and fractional PID controllers. Fractional PID controller was designed to minimize integral of squared error (ISE) and toward the convergence of gain/order parameters. The PID controller response exhibited better time domain characteristics as compared to the fractional PID, but suffered from a maximum overshoot as well. In a physical bolted joint, clamp load loss and external service load overshoots may lead to joint failures. Maximum overshoot was totally eliminated by fractional PID controller, proving its safe applicability to the bolted joint system. By choosing a realistic set point for clamp load loss, the maximum permissible external service loading conditions were predicted successfully.

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References

  1. S.A. Nassar, P.H. Matin, Nonlinear strain hardening model for predicting clamp load loss in bolted joints. J. Mech. Des. 128(6), 1328–1336 (2006)

    Article  Google Scholar 

  2. T. Lambert, Effects of variations in the screw thread coefficient of friction on the clamping force of bolted connections. J. Mech. Eng. Sci. 4(4), 401–406 (1962)

    Article  Google Scholar 

  3. G. Fazekas, On optimal bolt preload. J. Eng. Ind. 98(3), 779–782 (1976)

    Article  Google Scholar 

  4. M. Groper, Measuring preload in fasteners. Exp. Tech. 9(1), 28–29 (1985)

    Article  Google Scholar 

  5. J.M. Monaghan, The influence of lubrication on the design of yield tightened joints. J. Strain Anal. Eng. Des. 26(2), 123–132 (1991)

    Article  Google Scholar 

  6. T. Duffey, Optimal bolt preload for dynamic loading. Int. J. Mech. Sci. 35(3–4), 257–265 (1993)

    Article  Google Scholar 

  7. N.G. Pai, D.P. Hess, Dynamic loosening of threaded fasteners. Noise Vib. Worldwide 35(2), 13–19 (2004)

    Article  Google Scholar 

  8. Y. Chen, B.M. Vinagre, Fractional-Order Systems and Controls: Fundamentals and Applications (Springer, Berlin, 2010)

    MATH  Google Scholar 

  9. S. Das, Functional Fractional Calculus (Springer Science & Business Media, Berlin, 2011)

    Book  Google Scholar 

  10. R. Sekhar, T. Singh, P. Shah, ARX/ARMAX modeling and fractional order control of surface roughness in turning nano-composites, in 2019 International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE) (IEEE, New York, 2019), pp. 97–102

    Google Scholar 

  11. P. Shah, R. Sekhar, Closed loop system identification of a DC motor using fractional order model, in 2019 International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE) (IEEE, New York, 2019), pp. 69–74

    Google Scholar 

  12. P. Shah, R. Sekhar, S. Agashe, Application of fractional PID controller to single and multi-variable non-minimum phase systems. Int. J. Recent Technol. Eng. 8(2), 2801–2811 (2019)

    Article  Google Scholar 

  13. E. Balc, l. Ozturk, S. Kartal, Dynamical behaviour of fractional order tumor model with caputo and conformable fractional derivative. Chaos, Solitons & Fractals 123, 43 – 51 (2019)

    Google Scholar 

  14. K. Fatmawati et al., A fractional model for the dynamics of competition between commercial and rural banks in Indonesia. Chaos, Solitons & Fractals 122, 32–46 (2019)

    Article  MathSciNet  Google Scholar 

  15. W. Ma, M. Jin, Y. Liu, X. Xu, Empirical analysis of fractional differential equations model for relationship between enterprise management and financial performance. Chaos, Solitons & Fractals 125, 17–23 (2019)

    Article  MathSciNet  Google Scholar 

  16. J.T. Machado, A.M. Lopes, Fractional-order modeling of a diode. Commun. Nonlinear Sci. Numer. Simul. 70, 343–353 (2019)

    Article  MathSciNet  Google Scholar 

  17. J.-D. Gabano, T. Poinot, H. Kanoun, LPV continuous fractional modeling applied to ultracapacitor impedance identification. Control Eng. Pract. 45, 86–97 (2015)

    Article  Google Scholar 

  18. Y. Wei, Q. Gao, Y. Chen, Y. Wang, Design and implementation of fractional differentiators, part I: system based methods. Control Eng. Pract. 84, 297–304 (2019)

    Article  Google Scholar 

  19. A. Maachou, R. Malti, P. Melchior, J.-L. Battaglia, A. Oustaloup, B. Hay, Nonlinear thermal system identification using fractional volterra series. Control Eng. Pract. 29, 50–60 (2014)

    Article  Google Scholar 

  20. A.P. Singh, D. Deb, H. Agarwal, On selection of improved fractional model and control of different systems with experimental validation. Commun. Nonlinear Sci. Numer. Simul. 79, 104902 (2019)

    Article  MathSciNet  Google Scholar 

  21. R. Sekhar, V. Jadhav, Effect of strain hardening rate on the clamp load loss due to an externally applied separating force in bolted joints. Indian J. Appl. Res. 1(10), 61–63 (2011)

    Article  Google Scholar 

  22. J.H. Bickford, An Introduction to the Design and Behavior of Bolted Joints (Dekker, 1995)

    Google Scholar 

  23. T. Poinot, J.-C. Trigeassou, Identification of fractional systems using an output-error technique. Nonlinear Dyn. 38(1–4), 133–154 (2004)

    Article  MathSciNet  Google Scholar 

  24. L. Chen, B. Basu, D. McCabe, Fractional order models for system identification of thermal dynamics of buildings. Energy Build. 133, 381–388 (2016)

    Article  Google Scholar 

  25. K.J. Åström, T. Hägglund, P.I.D. Controllers, Theory, Design, and Tuning, vol. 2 (Instrument society of America Research, Triangle Park, NC, 1995)

    Google Scholar 

  26. I. Podlubny, Fractional-order systems and fractional-order controllers, in UEF-03-94 (Institute of Experimental Physics of the Slovak Academy Science, Kosice, 1994), pp. 1–24

    Google Scholar 

  27. I. Podlubny, Fractional Differential Equations an Introduction to Fractional Derivatives, Fractional Differential Equations, to Methods of their Solution and Some of Their Applications (Academic Press, San Diego, 1999)

    MATH  Google Scholar 

  28. I. Podlubny, L. Dorcak, I. Kostial, On fractional derivatives, fractional-order dynamic systems and \(PI^{\lambda }D^{\mu }\)-controllers, in Proceedings of the 36th IEEE Conference on Decision and Control, 1997, vol. 5, pp. 4985–4990 (1997)

    Google Scholar 

  29. Y. Luo, Y.Q. Chen, C.Y. Wang, Y.G. Pi, Tuning fractional order proportional integral controllers for fractional order systems. J. Process Control 20, 823–831 (2010)

    Article  Google Scholar 

  30. H. Malek, Y. Luo, Y. Chen, Identification and tuning fractional order proportional integral controllers for time delayed systems with a fractional pole. Mechatronics 23(7), 746–754 (2013)

    Article  Google Scholar 

  31. P. Shah, S. Agashe, and A. Singh, Design of fractional order controller for undamped control system, in 2013 Nirma University International Conference on Engineering (NUiCONE), pp. 1–5 (2013)

    Google Scholar 

  32. R.S. Barbosa, J. Tenreiro Machado, A.M. Galhano, Performance of fractional PID algorithms controlling nonlinear systems with saturation and backlash phenomena. J. Vib. Control 13 (9-10), 1407–1418 (2007)

    Google Scholar 

  33. C.I. Muresan, S. Folea, G. Mois, E.H. Dulf, Development and implementation of an FPGA based fractional order controller for a DC motor. Mechatronics 23(7), 798–804 (2013)

    Article  Google Scholar 

  34. B.B. Alagoz, A. Ates, C. Yeroglu, Auto-tuning of PID controller according to fractional-order reference model approximation for DC rotor control. Mechatronics 23(7), 789–797 (2013)

    Article  Google Scholar 

  35. P. Shah, S. Agashe, Experimental analysis of fractional PID controller parameters on time domain specifications. Progress Fract. Different. Appl. 3, 141–154 (2017)

    Article  Google Scholar 

  36. P. Shah, S. Agashe, Review of fractional PID controller. Mechatronics 38, 29–41 (2016)

    Article  Google Scholar 

  37. P. Shah, S. Agashe, A.J. Kulkarni, Design of a fractional \(PI^\lambda D^\mu \) controller using the cohort intelligence method. Front. Inform. Technol. Electron. Eng. 19, 437–445 (2018)

    Article  Google Scholar 

  38. P. Shah, A.J. Kulkarni, Application of variations of cohort intelligence in designing fractional PID controller for various systems, in Socio-Cultural Inspired Metaheuristics (Springer, Berlin, 2019), pp. 175–192

    Google Scholar 

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Correspondence to Pritesh Shah .

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Shah, P., Sekhar, R. (2021). Predictive Modeling and Control of Clamp Load Loss in Bolted Joints Based on Fractional Calculus. In: Thampi, S.M., Gelenbe, E., Atiquzzaman, M., Chaudhary, V., Li, KC. (eds) Advances in Computing and Network Communications. Lecture Notes in Electrical Engineering, vol 735. Springer, Singapore. https://doi.org/10.1007/978-981-33-6977-1_2

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  • DOI: https://doi.org/10.1007/978-981-33-6977-1_2

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