Investigation on formation mechanism of surface texture and modeling of surface roughness with internal gear power honing
In order to understand the formation mechanism of surface arc texture of internal gear power honing, the contact line equation of tooth surface is derived with homogeneous coordinate transformation method, which is based on the involute helical tooth surface equation of honed workpiece gear as well as the meshing principle of space surface. Then the influence of shaft angle Σ on the formation of surface arc texture of workpiece is analyzed. The results of imager and three-dimensional (3D) profilometer are in accordance with the theoretical derivation. In order to analyze the influence of the power honing process parameters on the surface roughness, a central composite surface design method based on response surface methodology (RSM) is proposed in a range of process parameters. Based on this, a regression model of surface roughness for the honed workpiece gear is established. Meanwhile, the influence of power honing process parameters on the surface roughness of honed workpiece gear is analyzed. The process parameters are the honing wheel speed nH, Z feed fZ, and X feed fX. The three-dimensional profilometer is utilized to analyze the roughness value with a set of process parameters, based on which the accuracy and reliability of the model can be verified. The results show that the surface arc texture is suitable for producing dense textures when the shaft angle Σ is within 5° to 10°. According to the analysis, nH has the greatest influence on the surface roughness of the honed workpiece gear. The effects of fZ and fX are almost the same when Ra ≥ 0.4 μm, and while Ra ≤ 0.3 μm, the effect of fZ is slightly higher than that of fX, Ra is the regression value obtained after the honing process. Based on the mention above, the surface quality of the honed workpiece gear can be predicted and controlled before honing.
KeywordsInternal gear power honing Surface arc texture Formation mechanism Regression model of roughness Response surface methodology (RSM)
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This study is mainly funded by the National Natural Science Foundation of China (Grant No.51575154), and the Major National R&D Projects (Grant No.2013ZX 04002051).
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