表面光洁度
表征(材料科学)
标准差
纹理(宇宙学)
材料科学
表面粗糙度
曲面(拓扑)
波长
长度测量
计算机科学
人工智能
光学
图像(数学)
统计
数学
几何学
纳米技术
复合材料
光电子学
物理
作者
Arun Prasanth Nagalingam,Moiz Sabbir Vohra,Pulkit Kapur,Song Huat Yeo
出处
期刊:Applied sciences
[Multidisciplinary Digital Publishing Institute]
日期:2021-05-31
卷期号:11 (11): 5089-5089
被引量:26
摘要
Surface texture characterization of components built using additive manufacturing (AM) remains a challenge. The presence of various asperities and random roughness distributions across a surface poses several challenges to users in selecting an appropriate cut-off wavelength (λc), evaluation length (ln), and measurement area. This paper investigates a modified framework for surface texture characterization of AM components. First, the surface asperities in an AM component were identified through scanning electron microscope (SEM) analyses. The maximum diameter (φm) of the surface asperities were determined through image processing and were used as cut-off for surface texture evaluation. Second, another set of surface texture results were extracted using standard measurement procedures per ISO 4287, 4288, 25178-1, -2, and -3. Third, the investigative measurement framework’s effectiveness and suitability were explored by comparing the results with ISO standard results. Last, the effects of using non-standard cut-off wavelength, evaluation length, and measurement area during surface texture characterization were studied, and their percentage deviations from the standard values were discussed. The key findings prove that (a) the evaluation length could be compromised instead of cut-off, (b) measurement area must be 2.5 times the maximum asperity size present in the surface, and (c) it is possible to identify, distinguish, and evaluate specific features from the AM surface by selecting appropriate filters, thereby characterizing them specifically. The investigations and the obtained results serve as valuable data for users to select appropriate measurement settings for surface texture evaluation of AM components.
科研通智能强力驱动
Strongly Powered by AbleSci AI