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Reduction of edge effect using response surface methodology and artificial neural network modeling of a spur gear treated by induction with flux concentrators

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Abstract

The aim of the study is to determine the effect of each parameter involved in the induction heating process on the final temperature distribution and case depth dispersion of a spur gear placed between two other gears having identical shapes and acting as a flux concentrator using two different approaches. The purpose of flux concentrators is to adjust the heat distribution in the part at the end of the heating process and to produce a better case depth between the middle and the edge of the gear. Mechanical properties of the gear could be improved by minimizing the edge effect at the tooth; thus, the optimization of temperature gradient between the middle and the edge plan by varying geometrical and machine parameters was studied. Two structured and comprehensive approaches to design an efficient model based on analysis of variance (ANOVA) and artificial neural networks (ANN) for the estimation of quality and the prediction of temperature profiles and edge effect was developed. The obtained results demonstrate that the statistical model was able to predict accurately the behavior of temperature and case depth distribution. In the final phase, several experimental tests were conducted on the induction machine to validate the simulation results and the prediction model.

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References

  1. Barka N, Bocher P, Brousseau J (2013) Sensitivity study of hardness profile of 4340 specimen heated by induction process using axisymmetric modeling. Int J Adv Manuf Technol 69(9–12):2747–2756

    Article  Google Scholar 

  2. Rudnev V, Loveless D, Cook R (2017) Handbook of Induction Heating. Boca Raton: CRC Press. https://doi.org/10.1201/9781315117485

  3. Dmytro R, Krause C, Nürnberger F, Bach FW, Gerdes L, Breidenstein B (2012) Investigation of the surface residual stresses in spray cooled induction hardened gearwheels. Int J Mater Res 103(1):73–79

    Article  Google Scholar 

  4. Jiang C, Chen H, Wang Q, Li Y (2016) Effect of brazing temperature and holding time on joint properties of induction brazed WC-Co/carbon steel using Ag-based alloy. J Mater Process Technol 229:562–569

    Article  Google Scholar 

  5. Guerrier P, Kirstein Nielsen K, Menotti S, Henri Hattel J (2016) An axisymmetrical non-linear finite element model for induction heating in injection molding tools. Finite Elem Anal Des 110:1–10

    Article  Google Scholar 

  6. Guerrier P, Tosello G, Nielsen KK, Hattel JH (2015) Three-dimensional numerical modeling of an induction heated injection molding tool with flow visualization. Int J Adv Manuf Technol 85(1–4):643–660

    Google Scholar 

  7. Barka N (2017) Study of the machine parameters effects on the case depths of 4340 spur gear heated by induction—2D model. Int J Adv Manuf Technol 93:1173–1181

    Article  Google Scholar 

  8. Fu X, Wang B, Zhu X, Tang X, Ji H (2016) Numerical and experimental investigations on large-diameter gear rolling with local induction heating process. Int J Adv Manuf Technol 91(1–4):1–11

    Google Scholar 

  9. Hammi H, El Ouafi A, Barka N, Chebak A (2017) Scanning based induction heating for AISI 4340 steel spline shafts-3D simulation and experimental validation. Advances in Materials Physics and Chemistry 07(06):263–276

    Article  Google Scholar 

  10. Haimbaugh RE 2015 Practical induction heat treating. ASM International p. 379

  11. Savaria V, Bridier F, Bocher P (2016) Predicting the effects of material properties gradient and residual stresses on the bending fatigue strength of induction hardened aeronautical gears. Int J Fatigue 85:70–84

    Article  Google Scholar 

  12. Rao DHMSB 2003 Experimental characterization of bending fatigue strength in gear teeth. Gear Technology 20(1):25–32

  13. Tong D, Gu J, Totten GE (2018) Numerical investigation of asynchronous dual-frequency induction hardening of spur gear. Int J Mech Sci 142-143:1–9

    Article  Google Scholar 

  14. Faizabadi MJ, Khalaj G, Pouraliakbar H, Jandaghi MR (December 01 2014) Predictions of toughness and hardness by using chemical composition and tensile properties in microalloyed line pipe steels. Neural Comput & Applic 25(7):1993–1999

    Article  Google Scholar 

  15. KOHLI A, SINGH H (April 01 2011) Optimization of processing parameters in induction hardening using response surface methodology. Sadhana 36(2):141–152

    Article  Google Scholar 

  16. Kranjc M, Županič A, Jarm T, and Miklavčič D 2009 Optimization of induction heating using numerical modeling and genetic algorithm, 2009 35th Annual Conference of IEEE Industrial Electronics, pp. 2104–2108. https://doi.org/10.1109/IECON.2009.5415323

  17. Stich TJ, Spoerre JK, Velasco T (2000) The application of artificial neural networks to monitoring and control of an induction hardening process. J Ind Technol 16(1):1–11

    Google Scholar 

  18. Midea SJ and Lynch P 2014 Tooth-by-tooth induction hardening of gears (and how to avoid some common problems). In: Proc. Thermal Process. Gear Solutions 46–51

  19. Li F, Li X, Qin X, Rong YK (May 01 2018) Study on the plane induction heating process strengthened by magnetic flux concentrator based on response surface methodology. J Mech Sci Technol 32(5):2347–2356

    Article  Google Scholar 

  20. Sabeeh HF, Abdulbaqi IM, and Mahdi SM 2018 Effect of flux concentrator on the surface hardening process of a steel gear, 2018 1st International Scientific Conference of Engineering Sciences - 3rd Scientific Conference of Engineering Science (ISCES), Diyala, pp. 80–85. https://doi.org/10.1109/ISCES.2018.8340532

  21. Rudnev V 2004 An objective assessment of magnetic flux concentrators. Heat treating progress p. 19–23

  22. Barka N, Chebak A, El Ouafi A, Jahazi M, Menou A (2014) A new approach in optimizing the induction heating process using flux concentrators: application to 4340 steel spur gear. J Mater Eng Perform 23(9):3092–3099

    Article  Google Scholar 

  23. Kleijnen JPC, 2014 Response surface Methodology. In: Fu M. (eds) Handbook of Simulation Optimization. International Series in Operations Research & Management Science, vol 216. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1384-8_4

  24. Box, G. E., & Draper, N. R. (2007). Response surfaces, mixtures, and ridge analyses (Vol. 649). John Wiley & Sons.

  25. Hagan MT, Demuth HB, Beale MH and De Jesús, O (1996) Neural network design, (Vol. 20). Boston: PWS Pub.

  26. Schmidhuber J (2015) Deep learning in neural networks: an overview, Neural Networks 61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003

  27. Alban LE (1985) Systematic analysis of gear failures. ASM International p. 232

  28. Draper NR and Smith H 2014 Applied regression analysis. Volume 326 Wiley Series in Probability and Statistics. Edition 3, John Wiley & Sons, p. 736

  29. Coto B, Navas VG, Gonzalo O, Aranzabe A, Sanz C (2011) Influences of turning parameters in surface residual stresses in AISI 4340 steel. Int J Adv Manuf Technol 53(9):911–919, 2011/04/01 2011

    Article  Google Scholar 

  30. Faraway JJ (2002) Practical regression and ANOVA using R, University of Bath. pp. 108–109

  31. Khalaj G, Pouraliakbar H, Mamaghani KR, Khalaj MJ (2013) Modeling the correlation between heat treatment, chemical composition and bainite fraction of pipeline steels by means of artificial neural networks. Neural Network World 23(4):351–368

    Article  Google Scholar 

  32. Ilyes Maamri NB, Elouafi A (2018) ANN laser hardening quality modeling using geometrical and punctual characterizing approaches. Coatings 8(6)

  33. Pontes FJ, Ferreira JR, Silva MB, Paiva AP, Balestrassi PP (2010) Artificial neural networks for machining processes surface roughness modeling. Int J Adv Manuf Technol 49(9):879–902, 2010/08/01

    Article  Google Scholar 

  34. Khalaj G, Nazari A, Pouraliakbar H (2013) Prediction of martensite fraction of microalloyed steel by artificial neural networks. Neural Network World 23(2):117–130

    Article  Google Scholar 

  35. Palanisamy P, Rajendran I, Shanmugasundaram S (2008) Prediction of tool wear using regression and ANN models in end-milling operation. Int J Adv Manuf Technol 37(1):29–41, 2008/04/01

    Article  Google Scholar 

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Authors and Affiliations

  1. Département de mathématiques, d’informatique et de génie, Université du Québec à Rimouski, Rimouski, Québec, Canada

    Mohamed Khalifa, Noureddine Barka & Jean Brousseau

  2. Département de génie mécanique, École de technologie supérieure, Montreal, Québec, Canada

    Mohamed Khalifa & Philippe Bocher

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  1. Mohamed Khalifa
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  2. Noureddine Barka
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  3. Jean Brousseau
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  4. Philippe Bocher
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Correspondence to Mohamed Khalifa.

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Khalifa, M., Barka, N., Brousseau, J. et al. Reduction of edge effect using response surface methodology and artificial neural network modeling of a spur gear treated by induction with flux concentrators. Int J Adv Manuf Technol 104, 103–117 (2019). https://doi.org/10.1007/s00170-019-03817-9

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  • DOI: https://doi.org/10.1007/s00170-019-03817-9

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