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Abstract

The article presents the methodology to estimate the operator influence on measurements performed with a coordinate measuring arm. The research was based on the R&R analysis, adapted to the specifics of redundant devices such as ACMM (selection of a test object difficult to measure). The method provides for measurements by three operators, who measure ten parts in two or three samples (measurement data developed in the article relate to the three measurements of holes). The methodology is designed to identify which operator has the best predisposition to perform measurements (generates the smallest measurement errors). Statistica software was used to analyse and visualize measurement data.

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Bibliography

[1] VDI/VDE 2617 – Accuracy of coordinate measuring machines – characteristics and their testing. VDI/VDE, 2011 (in German).
[2] ASME B89.4.22 – 2004 Method for Performance Evaluation of Articulated Arm Coordinate Measuring Machines. ASME, 2004.
[3] ISO 10360-12 Geometrical Product Specifications (GPS) – Acceptance and reverification tests for coordinate measuring systems (CMS) – Part 12: Articulated arm coordinate measurement machines (CMM). ISO, 2016. Determination of the operator’s influence on measurements with AACMM 81
[4] D. González-Madruga, J. Barreiro, E. Cuesta, B. González, and S. Martínez-Pellitero. AACMM performance test: Influence of human factor and geometric features. Procedia Engineering, 69:442–448, 2014. doi: 10.1016/j.proeng.2014.03.010.
[5] E. Cuesta, A. Telenti, H. Patiño, B. J. Alvarez, D. A. Mantaras, and P. Luque. Development of a force sensor prototype integrated on a coordinate measuring arm. Procedia Engineering, 132:998–1005, 2015. doi: 10.1016/j.proeng.2015.12.588.
[6] E. Cuesta, D.A. Mantaras, P. Luque, B. J. Alvarez, and D. Muina. Dynamic deformations in coordinate measuring arms using virtual simulation. International Journal of Simulation Modelling, 14(4):609–620, 2015. doi: 10.2507/IJSIMM14(4)4.311.
[7] S. Martínez-Pellitero, J. Barreiro, E. Cuesta, and B. J. Álvarez. A new process-based ontology for KBE system implementation: application to inspection process planning. The International Journal of Advanced Manufacturing Technology, 57(1-4):325, 2011. doi: 10.1007/s00170-011-3285-7.
[8] J. Sładek. Accuracy of Coordinate Measurements. Publishing House of Cracow University of Technology, Cracow, Poland, 2011 (in Polish).
[9] Measurement system analysis. Chrysler Group LLC, Ford Motor Company, General Motors Corporation, 2010.
[10] K. Ostrowska, D. Szewczyk, and J. Sładek. Determination of operator’s impact on the measurement done using coordinate technique. Advances in Science and Technology Research Journal, 7(20):11–16, 2013.
[11] T.D. Doiron. Dimensional measurement uncertainty from data. Part 2: Uncertainty R&R. International Journal of Metrology, 2016.
[12] Dell Inc. Dell Statistica (data analysis software system), volume 16. software.dell.com, 2016.
[13] M. Melichar, D. Kubátová, and J. Kutlwašer. CMM measuring cycle and human factor. In Proceeding of the 27th DAAAM International Symposium, pages 371–376, 2016. doi: 10.2507/27th.daaam.proceedings.055.
[14] G. Constable and E. Gasper. Conducting an R&R study yields information about measurement systems. Quality, 53:28–30, 2014.
[15] J. Minix, H. Chapman, N. Joshi, and A. Zargari. An investigation of measurement uncertainty of coordinate measuring machines (CMMs) by comparative analysis. The Journal of Technology Studies, 42(1):54–64, 2016. https://www.jstor.org/stable/90018737.
[16] ISO/TS 23165:2006(E) – Geometrical product specifications (GPS) – Guidelines of the evaluation of coordinate measuring machine (CMM) test uncertainty. ISO, 2006.
[17] K. Ostrowska, A.Gąska, and J. Sładek. Determining the uncertainty of measurement with the use of a virtual coordinate measuring arm. The International Journal of Advanced Manufacturing Technology, 71(1-4):529–537, 2014. doi: 10.1007/s00170-013-5486-8.
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Authors and Affiliations

Sławomir Jurkowski
1

  1. Technical Institute, State University of Applied Sciences in Nowy Sącz, Nowy Sącz, Poland.
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Abstract

We present computer simulations of a two-way ANOVA gage R&R study to determine the effects on the average speckle width of intensity patterns caused by scattered light reflected from random rough surfaces with different statistical characteristics. We illustrate how to obtain reliable computer data that properly simulate experimental measurements by means of the Fresnel diffraction integral, which represents an accurate analytical model for calculating the propagation of spatially-limited coherent beams that have been phase-modulated after being reflected by the vertical profiles of the generated surfaces. For our description we use four differently generated vertical profiles and five different vertical randomly generated roughness values.

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Authors and Affiliations

Moisés Cywiak
David Cywiak
Etna Yáñez

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