文章基本信息
- 标题：Optimized Routine of Machining Distortion Characterization Based on Gaussian Surface Curvature
- 本地全文：下载
- 作者：Garcia, Destiny R. ; Linke, Barbara S. ; Farouki, Rida T. 等
- 期刊名称：OASIcs : OpenAccess Series in Informatics
- 电子版ISSN：2190-6807
- 出版年度：2021
- 卷号：89
- 页码：5:1-5:17
- DOI：10.4230/OASIcs.iPMVM.2020.5
- 出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
- 摘要：Machining distortion presents a significant problem in products with high residual stresses from materials processing and re-equilibration after machining removes a large part of the material volume and is common in the aerospace industries. While many papers research on mechanisms of machining distortion, few papers report on the measurement, processing and characterization of distortion data. Oftentimes only line plot data is used to give a maximum distortion value. This paper proposes a method of measurement tool selection, measurement parameter selection, data processing through filtering and leveling, and use of Bézier Surfaces and Gaussian Curvature for distortion characterization. The method is demonstrated with three sample pieces of different pocket geometry from quenched aluminum. It is apparent that samples with machining distortion can have complex surface shapes, where Bézier Surfaces and Gaussian Curvature provide more information than the commonly used 2D line plot data.
- 关键词：Machining distortion; Metrology; Gaussian curvature
