Engineering Transactions, 66, 3, pp. 339–352, 2018
10.24423/EngTrans.860.20180830

Prediction of Surface Roughness of End Milling for Cycloidal Gears Based on Orthogonal Tests

Shan-Ming LUO
Xiamen University of Technology
China

Long-Xing LIAO
Xiamen University of Technology
China

Jing-Yu MO
Xiamen University of Technology
China

End milling method is applied to machining of cycloidal gears to improve the cutting quality and efficiency. The influence of milling parameters on the surface roughness is investigated based upon orthogonal tests with the four factors and four levels, as well as analysis of range and variance. A model to predict the surface roughness is built up on basis of the probability statistics and multivariate nonlinear regression analysis. Significance tests are conducted on the prediction model, and the interactive effect of these parameters on the surface roughness is figured out so as to propose optimization schemes. The results show that the shaft inclination angle has the biggest impact on the surface roughness, followed by the feed per tooth, the radial feed and the spindle speed. The prediction model of surface roughness is proved to have high prediction accuracy. This study aims to provide references for the improvement of machining quality of cycloidal gears and optimization of milling parameters.
Keywords: cycloidal gear; orthogonal test; milling parameters; surface roughness; prediction model
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References

Luo S.M., Liao L.X., Wang Y., A method of cycloidal gear machining, 105665838A, China, 2016.

Gao Q., Gong D.Y., Zhou G.Y., Experimental study on surface roughness in micromilling of single crystal Ni3Al-based superalloy [in Chinese], Chinese Journal of Mechanical Engineering, 27(6): 801–804, 2016.

Xie X.Y., Experimental research on surface roughness while milling 3Cr2NiMo die steel at high speed, Hydromechatronics Engineering, 42(15): 150–153, 2014.

Zhao G., Zhang Y. J., Sun Y.W., Study on cutting surface quality of Fe-based amorphous coating [in Chinese], Manufacturing Technology & Machine Tool, 10: 123–126, 2014.

Mandal N., Doloi B., Monda B., Surface roughness prediction model using Zirconia Toughened Alumina (ZTA) turning inserts: Taguchi Method and Regression Analysis, Journal of the Institution of Engineers (India): Series C, 97(1): 77–84, 2016.

Li Y.P., Li C.H., Zhao X.F., Experimental research on surface roughness of 211Z aluminum alloy milling [in Chinese], Machinery Design & Manufacture, 2016(6): 78–80, 2016.

Huang X.M., Sun J., Research on surface roughness of 7050-T7451 aluminum alloy by high speed milling [in Chinese], Chinese Journal of Construction Machinery, 12(3): 248–251, 2014.

Wang Z.H., Yuan J.T., Liu T.T, Huang J., Liu L., Study on surface roughness in high-speed milling of AlMn1Cu using factorial design and partial least square regression, The International Journal of Advanced Manufacturing Technology, 76(9–12): 1783–1792, 2015.

Ibrahem M., Eltaib M.E.H., Sarhan A.D., Ei-Zahry R.M., Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining, The International Journal of Advanced Manufacturing Technology, 76(5–8): 1459–1467, 2015.

Sharkawy A.B., El-Sharief M.A., Soliman M.E.S., Surface roughness prediction in end milling process using intelligent systems, International Journal of Machine Learning and Cybernetics, 5(1): 135–150, 2014.

Markopoulos A.P., Georgiopoulos, S. Manolakos D.E., On the use of back propagation and radial basis function neural networks in surface roughness prediction, Journal of Industrial Engineering International, 12(3): 389–400, 2016.

Wang M.H., Li S.Y., Zheng Y.H., Surface roughness of titanium alloy under ultrasonic vibration milling [in Chinese], Transactions of the Chinese Society of Agricultural Machinery, 45(6): 341–346, 2014.

Kalidass S., Palanisamy P., Prediction of surface roughness for AISI 304 steel with solid carbide tools in end milling process using regression and ANN models, Arabian Journal for Science and Engineering, 39(11): 8065–8075, 2014.

Sun L., Yang S.Y., Study on parameter design based on orthogonal test and support vector machine [in Chinese], Chinese Journal of Mechanical Engineering, 22(8): 971–975, 2011.

Wang X.S., Kang M., Fu X.Q., Li C.L., Prediction model of surface roughness in lenses precision turning [in Chinese], Chinese Journal of Mechanical Engineering, 49(15): 192–198, 2013.

Shi W.T., Liu Y.D., Wang X.B., Experiment and prediction model for surface roughness in micro-milling [in Chinese], Transactions of the Chinese Society of Agricultural Machinery, 41(1): 211–215, 2010.

Mahesh G., Muthu S., Devadasan S.R., Prediction of surface roughness of end milling operation using genetic algorithm, The International Journal of Advanced Manufacturing Technology, 77(1–4): 369–381, 2015.

Karkalos N.E., Galanis N.I., Markopoulso A.P., Surface roughness prediction for the milling of Ti-6Al-4V ELI alloy with the use of statistical and soft computing techniques, Measurement, 90(C): 25–35, 2016.

Qiu Y.B., Experimental design and data processing [in Chinese], Science and Technology of China Press, Beijing, 2008.

Zhang B., High-speed cutting technology and application [in Chinese], China Machine Press, Beijing, 2004.

Alauddin M., Baradie M.A.E, Hashmi M.S.J., Computer-aided analysis of a surfaceroughness model for end milling, Journal of Materials Processing Technology, 55(2): 123–127, 1995.




DOI: 10.24423/EngTrans.860.20180830

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