W. Liu, Xiuyan Zheng, Xinghua Jia, Li Fan, Shuangjun Liu, Zhenyuan Jia
{"title":"A measurement method for roughness of micro-heterogeneous surface in deep hole","authors":"W. Liu, Xiuyan Zheng, Xinghua Jia, Li Fan, Shuangjun Liu, Zhenyuan Jia","doi":"10.1109/ICICIP.2010.5564171","DOIUrl":null,"url":null,"abstract":"Due to the inherent limitations of structure and dimensional, it is difficult to measure the surface roughness of micro-heterogeneous surface in deep hole. In this paper, the microscopic image of micro-heterogeneous surface is obtained by the long working distance lenses of digital microscopic camera, firstly. Thereafter, two artificial neural network models, which take microscopic image features as the inputs, are presented to measure the surface roughness. Then, experiments on the microscopic image acquisition and roughness calibration are conducted. Finally, the analysis results indicate that the proposed measurement method is efficient and effective for evaluating the microcosmic surface roughness of micro-heterogeneous surface in deep hole.","PeriodicalId":152024,"journal":{"name":"2010 International Conference on Intelligent Control and Information Processing","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Intelligent Control and Information Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICIP.2010.5564171","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Due to the inherent limitations of structure and dimensional, it is difficult to measure the surface roughness of micro-heterogeneous surface in deep hole. In this paper, the microscopic image of micro-heterogeneous surface is obtained by the long working distance lenses of digital microscopic camera, firstly. Thereafter, two artificial neural network models, which take microscopic image features as the inputs, are presented to measure the surface roughness. Then, experiments on the microscopic image acquisition and roughness calibration are conducted. Finally, the analysis results indicate that the proposed measurement method is efficient and effective for evaluating the microcosmic surface roughness of micro-heterogeneous surface in deep hole.