A new method of layered superposition reconstruction modeling on grinding-shot peening surfaces. (1st December 2022)

• Record Type: Journal Article
• Authors: Tang, Jinyuan; Chen, Jiling; Yang, Duo; Li, Lin; Zhao, Jiuyue; Guo, Minzhi
• Is Part Of: Surface topography. Volume 10:Number 4(2022)
• Publication Date: 2022-12-01
• Subjects: modeling -- rough surface -- shot peening -- stratified surfaces -- grinding; Surfaces (Physics) -- Periodicals; Surfaces (Physics) -- Measurement -- Periodicals; 530.417
• DOI: 10.1088/2051-672X/ac9ace
• Languages: English
• ISSNs: 2051-672X

Abstract

Abstract: Shot peening for semi-finished or finished parts is a high-performance precision manufacturing process, and the final micro-surface morphology has layered superposition features, that is, the original surface features and shot peening features coexist on the part surface. The existing layered superposition theory is based on the idea that the surface is superimposed under different baselines, but it cannot characterize the two-process surface where the characteristic baseline cannot be separated after shot peening. To solve this problem, a new layered surface superposition modeling method is proposed in this paper with the grinding-shot peening surface as the research object. In this method, the main texture features of the grinding surface are extracted by B-spline surface fitting, and the spatial distribution of craters in the shot peening surface is adjusted by this texture feature. The initial profile of the grinding-shot peening surface is constructed according to the Pawlus superposition theory, and its height parameters are adjusted by the time-frequency iteration method. In this paper, the modeling method is verified by the measured grinding-shot peening surfaces with a coverage of 100% and 200%. The results show that this method can not only ensure the uniformity of crater texture on the reconstructed surface, but also ensure that the maximum error of spatial parameters is less than 2%. Moreover, the symmetrical autocorrelation functions of the reconstructed surface are consistent with the characteristics of the measured surface.