Abstract
Isotropy is an important feature of an area filter in the three-dimensional surface roughness evaluation. First, the transmission characteristic deviation between the approximating spline filter and the Gaussian filter is reduced by cascading approximating. Second, the approximating spline filter is superimposed on the orthogonal direction to obtain an isotropic areal filter. Then, four direct methods for the solving approximating spline matrix are applied. Based on the profile filtering and areal filtering, the computational efficiency and accuracy are compared. The experimental results show that the improved square root method (LDLT decomposition) combines both computational efficiency and filtering precision, and is a good choice for solving the approximating spline matrix. Finally, six kinds of robust approximating spline filters are constructed. Taking the output value of robust Gaussian regression filter (RGRF) as reference, and the honing profile and step surface with deep valley characteristics were used as test surfaces to compare their robustness and iteration time. The experimental results show that the approximating spline filter based on the ADRF function has the shortest iteration times, while its roughness is close to the robust Gaussian regression filter.
Highlights
Surface roughness is an important predictor of crack, corrosion, and fatigue damage of mechanical parts, and it represents a tradeoff between the manufacturing cost and performance of mechanical parts [1]
The experimental results show that the approximating spline filter based on the ADRF function has the shortest iteration times, while its roughness is close to the robust Gaussian regression filter
The cubic spline filter has the advantage of suppressing the end effect, there is a great difference in the transmission characteristics between the spline filter and the Gaussian filter
Summary
Surface roughness is an important predictor of crack, corrosion, and fatigue damage of mechanical parts, and it represents a tradeoff between the manufacturing cost and performance of mechanical parts [1]. Krystek first applied the numerical solution method to the spline function matrix, which greatly improved the computational efficiency of spline filters [15]. The direct method is applied to approximating spline filter to compare their performance in the profile and areal filtering. Direct methods for solving an approximate spline matrix are derived and compared in terms of computational efficiency and accuracy, and relevant suggestions are provided. Six robust approximating spline filters are constructed and used in the experiments, which take honing profile and step surface with deep valley characteristics as experimental objects. The robust approximating spline filter based on ADRF function has higher computational efficiency, and has robustness close to that of the robust Gaussian regression filter. This work studies and improves the performance of approximating spline filter, which is helpful to promote the further application of approximating spline filter
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