five

Data for "Improved Characterisation (ImC) of structured surfaces"

收藏
Figshare2025-12-08 更新2026-04-28 收录
下载链接:
https://figshare.com/articles/dataset/Data_for_Improved_Characterisation_ImC_of_structured_surfaces_/30826133
下载链接
链接失效反馈
官方服务:
资源简介:
Data for paper: "Improved Characterisation (ImC) of structured surfaces"Surface characterisation, commonly used to assess the properties of a manufactured part, plays a crucial role in surface metrology. While most existing surface characterisation methods are focused on comparing the measured surface to a design surface according to their fixed surface-defining parameters, they seem to struggle to quantify how the surface-defining parameters deviate from their design values, primarily due to the uncertainties introduced in the manufacturing processes (compared to the low uncertainty in the measurement processes). Such parameter deviations are critical for evaluating the functional performance of manufactured parts. This is particularly true for structured surfaces, where repeated elements contribute to the final functional performance in an average manner. In this paper, an improved characterisation (ImC) method for structured surfaces is proposed. The ImC method combines six degrees of freedom transformation with the degrees of freedom from the surface-defining parameters to best fit the design surface to the measured surface, rather than relying solely on six degrees of freedom transformation to register with the design surface. The ImC method, implemented using the digital optimisation method, is verified using both simulated and measured surfaces with different types of structures, including a blazed grating, one-dimensional and two-dimensional sinusoidal surfaces, and a microlens array surface. Results show that the ImC method is accurate and effective, as well as notably generic, and can be applied to a broad range of applications in surface characterisation, even beyond structured surfaces.
创建时间:
2025-12-08
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作