Neuber rule and Arola-Ramulu model are widely used to predict the stress concentration factor of rough specimens. However, the height parameters and effective valley radius used in these two models depend strongly on ...Neuber rule and Arola-Ramulu model are widely used to predict the stress concentration factor of rough specimens. However, the height parameters and effective valley radius used in these two models depend strongly on the resolution of the roughness-measuring instruments and are easily introduce measuring errors. Besides, it is difficult to find a suitable parameter to characterize surface topography to quantitatively describe its effect on stress concentration factor. In order to overcome these disadvantages, profile moments are carried out to characterize surface topography, surface topography is simulated by superposing series of cosine components, the stress concentration factors of different micro cosine-shaped surface topographies are investigated by finite element analysis. In terms of micro cosine-shaped surface topography, an equation using the second profile moment to estimate the stress concentration factor is proposed, predictions for the stress concentration factor using the proposed expression are within 10% error compared with the results of finite element analysis, which are more accurate than other models. Moreover, the proposed equation is applied to the real surface topography machined by turning. Predictions for the stress concentration factor using the proposed expression are within 10% of the maximum stress concentration factors and about 5% of the effective stress concentration factors estimated from the finite element analysis for three levels of turning surface topographies under different simulated scales. The proposed model is feasible in predicting the stress concentration factors of real machined surface topographies.展开更多
基金Supported by National Defense Preliminary Research Project of China(Grant No.104010205)
文摘Neuber rule and Arola-Ramulu model are widely used to predict the stress concentration factor of rough specimens. However, the height parameters and effective valley radius used in these two models depend strongly on the resolution of the roughness-measuring instruments and are easily introduce measuring errors. Besides, it is difficult to find a suitable parameter to characterize surface topography to quantitatively describe its effect on stress concentration factor. In order to overcome these disadvantages, profile moments are carried out to characterize surface topography, surface topography is simulated by superposing series of cosine components, the stress concentration factors of different micro cosine-shaped surface topographies are investigated by finite element analysis. In terms of micro cosine-shaped surface topography, an equation using the second profile moment to estimate the stress concentration factor is proposed, predictions for the stress concentration factor using the proposed expression are within 10% error compared with the results of finite element analysis, which are more accurate than other models. Moreover, the proposed equation is applied to the real surface topography machined by turning. Predictions for the stress concentration factor using the proposed expression are within 10% of the maximum stress concentration factors and about 5% of the effective stress concentration factors estimated from the finite element analysis for three levels of turning surface topographies under different simulated scales. The proposed model is feasible in predicting the stress concentration factors of real machined surface topographies.
