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Computational Metrology

Published

Author(s)

Ted Hopp

Abstract

Coordinate measuring systems (CMSs) assess length-based characteristics of mechanical parts by measuring points on the surface and analyzing the point data. Data analysis software can contribute significantly to the total measurement error of a CMS. Factors affecting software performance include the choice of analysis method, the quality of the software, and characteristics of the specific measurement task. By computational metrology we mean the study of how data analysis factors affect the measurement uncertainty of a CMS.

This paper describes research at NIST on computational metrology. Metrological questions fall into two categories: (1) the proper choice of data analysis objective for a particular application and (2) the performance of the implementation. Our research goal is to develop the basis for a national standard in the U.S. for CMS software performance evaluation. We are implementating a Special Test service to be offered through the NIST Measurement Services Program. The service is based on a black-box model of softwawre, in which the internal structure of the software and the choice of solution methods are assumed to be unknown. The model indentifies a number of error sources for data analysis software. We are designing test methods for identifying the various components of the model and how those components relate to measurement uncertainty in inspection applications.

Citation
Manufacturing Review
Volume
6:4

Keywords

coordinate measure systems, CMS, mechanical parts, metrology

Citation

Hopp, T. (1993), Computational Metrology, Manufacturing Review, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=821173 (Accessed April 14, 2024)
Created September 1, 1993, Updated February 17, 2017