A Gear Form-Grinding Optimization Method Based on Neural Network

Abstract

Since the contact line equation is a transcendental equation, the relationship between the installation angle and the shape could not be expressed by explicit functions, which made it difficult to obtain the optimal shape, this paper firstly took three evaluation parameters of the shape, overrun, shift and offset as the objective function as well as the installation angle as variables the contact line optimization model was establish. Secondly, a neural network was introduced to solve the evaluation parameters. Through training the neural network by setting the installation angle as the input, the evaluation parameters as the output, the results show that the trained neural network can respond correctly, and has the advantages which the other method do not obtain. As an example of end relief modified helical gear, the results shows that the method can reduce the grinding errors effectively. Finally, the grinding experiments proven the effectiveness of the method.

Keywords

• Gear
• Gear manufacturing
• Form-grinding
• Optimization
• Neural network

References

1. Chen LL, Zhang H, Ni F. Present situation and development trend for construction of electric vehicle energy supply infrastructure. Power Syst Technol. 2011;35(14):11–17.
2. Geng Z, Li G. Optimal clutch control of a one-way clutch assistant transmission for electrical vehicles. Int J Auto Sci Tech. 2022;6(3):257-264.
3. Lu TL, Dai F, Zhang JW, Wu MX. Optimal control of dry clutch engagement based on the driver's starting intentions. Proc Instn Mech Eng, Part D: J Automob Eng. 2012;226(8):1048-1057.
4. Di X, Huang Y, Ge Y, Li G, Hu M. Fuzzy-PID speed control of diesel engine based on load estimation. SAE Int J Engines. 2015;8(4):1669-1677.
5. Huang Y, Wan G, Cui T, Li G. A study on engine control strategy for gear shifting of AMT. Automotive Engineering. 2012;34(3):245-248.
6. Jiang D, Huang Y, Li G, Hao D, Zuo Z. Design of a speed tracking controller for heavy-duty vehicles with an all-speed governor based on a model predictive control strategy. Int J Engine Res. 2017;18(9):930-940.
7. Li G, Zhu WD. Experimental investigation on control of an infinitely variable transmission system for tidal current energy converters. IEEE/ASME Trans Mechatron. 2021; 26(4):1960-1967.
8. Li G, Zhu WD. Theoretical and experimental investigation on an integral time-delay feedback control combined with a closed-loop control for an infinitely variable transmission system. Mech Mach Theory. 2021;164:104410.

9. Li X, Li G, Ren J, Li W. Numerical simulation of helical gear tooth root crack initiation life of high-speed EMUs. China Mech Eng. 2018;29(09):1017-1024.
10. Wang ZH, Wang J, Wang QL, Li G. Transmission error of spiral bevel gear based on finite element method. J of Vib Shock. 2014;33(14):165-170.
11. Hu YH, Li G, Zhu WD, Cui JK. An elastic transmission error compensation method for rotary vector speed reducers based on error sensitivity analysis. Appl Sci. 2020;10(2):481.
12. Yan J, Li G, Liu K. Development trend of wind power technology. Int J Adv Eng Res Sci. 2020;7(6):124-132.
13. Li G, Zhu WD. Time-delay closed-loop control of an infinitely variable transmission system for tidal current energy converters. Renew Energy. 2022;189:1120-1132.
14. Li G. Design and modeling of an impulse continuously variable transmission with a rotational swashplate. Int J Auto Sci Tech. 2020;4(4):307-313.
15. Xu M, Zhang X, Hu G, Li G. The structure design and flow field simulation of a fire water monitor driven by worm gear with bevel gear. Mach Tool & Hydra. 2016;6:57-61.
16. Gu KL, Wang ZH, Li G, Liu XR. Optimization of geometric parameters of the straight conjugate internal gear pump based on GA. Elec Sci Tech, 2017;30(6):39-42.
17. Zhang XL, Wang ZH, Li G. Research on virtual hobbing simulation and study of tooth surface accuracy of involute helical gears. Appl Mech Mater. 2012;155:601-605.
18. Wang ZH, Li G, Zhang XL, Li KS. Study on the virtual hobbing simulation and tooth surface accuracy of the entirety of involute helical gears. J Mech Trans. 2012;36(8): 9-13.
19. Li G, Wang ZH, Zhu WD, Kubo A. A function-oriented active form-grinding method for cylindrical gears based on error sensitivity. Int J Adv Manuf Tech. 2017;92(5-8):3019-3031.
20. Wang ZH, Zhu WM, Li G, Geng Z. Optimization of contact line for form-grinding modified helical gears based on neural network. China Mech Eng. 2014;25(12):1665-1671.
21. Li G. An active forming grinding method for cylindrical involute gears based on a second-order transmission error model. SCIREA J Mech Eng. 2019;2(1):1-14.
22. Li G, Zhu WD. An active ease-off topography modification approach for hypoid pinions based on a modified error sensitivity analysis method. ASME J Mech Des. 2019;141(9):093302.
23. Li G, Wang ZH, Kubo A. Error-sensitivity analysis for hypoid gears using a real tooth surface contact model. Proc Instn Mech Eng, Part C: J Mech Eng Sci. 2017;231(3):507-521.
24. Zhang WX, Wang ZH, Liu XR, Li G, Wan PL, Wang W. Research on optimization of temperature measuring point and thermal error prediction method of CNC machine tools. J Shaanxi University of Tech (Na Sci Ed). 2017; 33(3):18-24.
25. Wang ZH, Cao H, Li G, Liu XR. Compensation of the radial error of measuring head based on forming grinding machine. J Mech Trans. 2017;41(3):143-146.
26. Wang ZH, Song XM, He WM, Li G, Zhu WM, Geng Z. Tooth surface model construction and error evaluation for tooth-trace modification of helical gear by form grinding. China Mech Eng. 2015;26(21):2841-2847.
27. Li G, Wang ZH, Kubo A. Tooth contact analysis of spiral bevel gears based on digital real tooth surfaces. Chin J Mech Eng. 2014;50(15):1-11.
28. Wang ZH, Wang J, Ma PC, Li G. Dynamic transmission error analysis of spiral bevel gears with actual tooth surfaces. J Vib Shock. 2014;33(15):138-143.
29. Geng Z, Li G. A reliability-enhanced forming grinding method of cylindrical involute gears for electrical vehicles. Int J Auto Sci Tech. 2022;6(4):317-323.
30. Li G, Wang ZH, Kubo A. The modeling approach of digital real tooth surfaces of hypoid gears based on non-geometric-feature segmentation and interpolation algorithm. Int J Prec Eng Manuf. 2016;17(3):281-292.
31. Li G, Zhu WD. Design and power loss evaluation of a noncircular gear pair for an infinitely variable transmission. Mech Mach Theory. 2021;156:104137.
32. Wei XT, Zhu JP, Li G. Automatic NC Programming for chamfering addendum of spiral bevel gear based on UG/Open. Appl Mech Mater. 2013;365:950-954
33. Li G, Geng Z. Gear bending stress analysis of automatic transmissions with different fillet curves. Int J Auto Sci Tech. 2021;5(2):99-105.
34. Huang DQ, Wang ZH, Li G, Zhu WD. Conjugate approach for hypoid gears frictional loss comparison between different roughness patterns under mixed elastohydrodynamic lubrication regime. Tribo Int. 2019;140:105884.
35. Li G, Wang ZH, Zhu WD. Prediction of surface wear of involute gears based on a modified fractal method. ASME J Tribo. 2019;141(3):031603.
36. Wu J, Wang ZH, Li G. Study on crack propagation characteristics and remaining life of helical gear. J Mech Trans. 2014;38(12):1-4.
37. Li G, Wang ZH, Geng Z, Zhu WM. Modeling approach of digital real tooth surfaces of hypoid gears based on non-geometric-feature segmentation and interpolation algorithm. Chin J Mech Eng. 2015;51(7):77-84.
38. Li G, Geng Z. Tooth contact analysis of herringbone rack gears of an impulse continuously variable transmission. Int J Auto Sci Tech. 2021;5(1):52-57.
39. Wang ZH, Yuan KK, Li G. Optimization identification for dynamic characteristics parameters of sliding joints based on response surface methodology. China Mech Eng. 2016;27(5):622-626.
40. Hu YH, Li G, Hu AM. Iterative optimization of orbital dynamics based on model prediction. Front Arti Intel App. 2019;320:76-86.
41. Wan GQ, Huang Y, Zhang FJ, Li G. Integrated powertrain control for gear shifting. Appl Mech Mater. 2012;148: 725-730.
42. Zhou Y, Huang Z, Zhang F, Jing S, Chen Z , Ma Y, Li G, Ren H. Experimental study of WC–Co cemented carbide air impact rotary drill teeth based on failure analysis. Eng Fail Anal. 2013; 36, 186-198.