Measurement uncertainty evaluation of conicity error inspected on CMM View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-01

AUTHORS

Dongxia Wang, Aiguo Song, Xiulan Wen, Youxiong Xu, Guifang Qiao

ABSTRACT

The cone is widely used in mechanical design for rotation, centering and fixing. Whether the conicity error can be measured and evaluated accurately will directly influence its assembly accuracy and working performance. According to the new generation geometrical product specification(GPS), the error and its measurement uncertainty should be evaluated together. The mathematical model of the minimum zone conicity error is established and an improved immune evolutionary algorithm(IIEA) is proposed to search for the conicity error. In the IIEA, initial antibodies are firstly generated by using quasi-random sequences and two kinds of affinities are calculated. Then, each antibody clone is generated and they are self-adaptively mutated so as to maintain diversity. Similar antibody is suppressed and new random antibody is generated. Because the mathematical model of conicity error is strongly nonlinear and the input quantities are not independent, it is difficult to use Guide to the expression of uncertainty in the measurement(GUM) method to evaluate measurement uncertainty. Adaptive Monte Carlo method(AMCM) is proposed to estimate measurement uncertainty in which the number of Monte Carlo trials is selected adaptively and the quality of the numerical results is directly controlled. The cone parts was machined on lathe CK6140 and measured on Miracle NC 454 Coordinate Measuring Machine(CMM). The experiment results confirm that the proposed method not only can search for the approximate solution of the minimum zone conicity error(MZCE) rapidly and precisely, but also can evaluate measurement uncertainty and give control variables with an expected numerical tolerance. The conicity errors computed by the proposed method are 20%-40% less than those computed by NC454 CMM software and the evaluation accuracy improves significantly. More... »

PAGES

212-218

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.3901/cjme.2015.0831.107

DOI

http://dx.doi.org/10.3901/cjme.2015.0831.107

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1008003841


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