Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 2
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

Reliable measurement uncertainty is a crucial part of the conformance/nonconformance decision-making process in the field of Quality Control in Manufacturing. The conventional GUM-method cannot be applied to CMM measurements primarily because of lack of an analytical relationship between the input quantities and the measurement. This paper presents calibration uncertainty analysis in commercial CMM-based Coordinate Metrology. For the case study, the hole-plate calibrated by the PTB is used as a workpiece. The paper focuses on thermo-mechanical errors which immediately affect the dimensional accuracy of manufactured parts of high-precision manufacturers. Our findings have highlighted some practical issues related to the importance of maintaining thermal equilibrium before the measurement. The authors have concluded that the thermal influence as an uncertainty contributor of CMM measurement result dominates the overall budgets for this example. The improved calibration uncertainty assessment technique considering thermal influence is described in detail for the use of a wide range of CMM users.
Go to article

Bibliography

[1] International Organization for Standardization (2009). Geometrical product specifications (GPS) – Acceptance and reverification tests for coordinate measuring machines (CMM) – Part 2: CMMs used for measuring linear dimensions (ISO Standard No. 10360-2:2009). https://www.iso.org/standard/40954.html
[2] International Organization for Standardization (2017). Geometrical product specifications (GPS) – Inspection by measurement of workpieces and measuring equipment – Part 1: Decision rules for proving conformance or non-conformance with specifications (ISO Standard No. 14253-1:2017). https://www.iso.org/standard/70137.html
[3] Mussatayev, M., Huang, M.,&Tang, Zh., (2020). Current issues in uncertainty of dimensional tolerance metrology and the future development in the domain of tolerancing. IOP Conference Series: Materials Science and Engineering, 715(1). https://doi.org/10.1088/1757-899X/715/1/012084
[4] Leach, R., & Smith, S. T. (Eds.). (2018). Basics of Precision Engineering. CRC Press.
[5] David, F., & Hannaford, J. (2012). Good Practice Guide No. 80. National Physical Laboratory.
[6] International Organization for Standardization (2013). Geometrical product specifications (GPS) – Coordinate measuring machines (CMM): Technique for determining the uncertainty of measurement – Part 1: Overview and metrological characteristics (ISO Standard No. ISO/TS 15530-1). https://www.iso.org/standard/38693.html
[7] Płowucha, W. (2019). Point-straight line distance as model for uncertainty evaluation of coordinate measurement. Measurement, 135, 83–95. https://doi.org/10.1016/j.measurement.2018.11.008
[8] Mussatayev, M., Huang, M., & Beshleyev, S. (2020). Thermal influences as an uncertainty contributor of the coordinate measuring machine (CMM). The International Journal of Advanced Manufacturing Technology, 111, pp. 537–547. https://doi.org/10.1007/s00170-020-06012-3
[9] Sładek, J., & G˛aska, A. (2012). Evaluation of coordinate measurement uncertainty with use of virtual machine model based on Monte Carlo method. Measurement, 45(6), 1564–1575. https://doi.org/10.1016/j.measurement.2012.02.020
[10] Saunders, P., Verma, M., Orchard, N., & Maropoulos, P. (2013). The application of uncertainty evaluating software for the utilisation of machine tool systems for final inspection. 10th International Conference and Exhibition on Laser Metrology, Coordinate Measuring Machine and Machine Tool Performance, LAMDAMAP 2013, 219–228.
[11] International Organization for Standardization (2011). Geometrical product specifications (GPS) – Coordinate measuring machines (CMM): Technique for determining the uncertainty of measurement – Part 3: Use of calibrated workpieces or measurement standards (ISO Standard No. 15530-3). https://www.iso.org/standard/53627.html
[12] International Organization for Standardization (2004). Geometrical Product Specifications (GPS) – Coordinate measuring machines (CMM): Technique for determining the uncertainty of measurement – Part 3: Use of calibrated workpieces or standards (ISO Standard No. ISO/TS 15530-3). https://www.iso.org/standard/38695.html
[13] European Cooperation for Accreditation of Laboratories. (1995). Coordinate Measuring Machine Calibration [Publication Reference, EAL-G17].
[14] International Organization for Standardization. (2006). Geometrical product specifications (GPS) – Guidelines for the evaluation of coordinate measuring machine (CMM) test uncertainty (ISO Standard No. ISO/TS 23165). https://www.iso.org/standard/24236.html
[15] Fang, C. Y., Sung, C. K., & Lui, K. W. (2005). Measurement uncertainty analysis of CMM with ISO GUM. ASPE Proceedings, United States, 1758–1761.
[16] Płowucha, W. (2020). Point plane distances model for uncertainty evaluation of coordinate measurement. Metrology and Measurement Systems, 27(4), 625–639. https://doi.org/10.24425/mms.2020.134843
[17] Ruffa, S., Panciani, G. D., Ricci, F., & Vicario, G. (2013). Assessing measurement uncertainty in CMM measurements: comparison of different approaches. International Journal of Metrology and Quality Engineering, 4(3), 163–168. https://doi.org/10.1051/ijmqe/2013057
[18] Cheng Y. B., Chen X. H., & Li Y. R. (2020). Uncertainty Analysis and Evaluation of Form Measurement Task for CMM. Acta Metrologica Sinica, 41(2), 134–138. https://doi.org/10.3969/j.issn.1000-1158.2020.02.02 (in Chinese).
[19] Rost, K., Wendt, K., & Härtig, F. (2016). Evaluating a task-specific measurement uncertainty for gear measuring instruments via Monte Carlo simulation. Precision Engineering, 44, 220–230. https://doi.org/10.1016/j.precisioneng.2016.01.001
[20] Valdez, M. O.,&Morse, E. P. (2017). The role of extrinsic factors in industrial task-specific uncertainty. Precision Engineering, 49, 78–84. https://doi.org/10.1016/j.precisioneng.2017.01.013
[21] Yang, J., Li, G., Wu, B., Gong, J., Wang, J., & Zhang, M. (2015). Efficient methods for evaluating task-specific uncertainty in laser-tracking measurement. MAPAN-Journal Metrology Society of India, 30(2), 105–117. https://doi.org/10.1007/s12647-014-0126-9
[22] Haitjema, H. (2019). Calibration of displacement laser interferometer systems for industrial metrology. Sensors, 19(19), 4100. https://doi.org/10.3390/s19194100
[23] Doytchinov, I., Shore, P., Nicquevert, B., Tonnellier, X., Heather, A., & Modena, M. (2019). Thermal effects compensation and associated uncertainty for large magnet assembly precision alignment. Precision Engineering, 59, 134–149. https://doi.org/10.1016/j.precisioneng.2019.06.005
[24] Van Gestel, N. (2011). Determining measurement uncertainties of feature measurements on CMMs (Bepalen van meetonzekerheden bij het meten van vormelementen met CMMs) [Doctoral dissertation, Katholieke Universiteit Leuven]. Digital repository for KU Leuven Association. https://lirias.kuleuven.be/retrieve/157334 [25] Mussatayev, M., Huang, M., & Rysbayeva, G. (2019). Role of uncertainty calculation in dimensional metrology using Coordinate Measuring Machine. ARCTIC Journal, 72(6).
[26] International Organization for Standardization (2005). Test code for machine tools – Part 9: Estimation of measurement uncertainty for machine tool tests according to series ISO 230, basic equations (ISO Standard No. ISO/TR 230-9:2005). https://www.iso.org/standard/39165.html
[27] International Organization for Standardization (2008). Uncertainty of measurement-Part 3: Guide to the expression of uncertainty in measurement (GUM: 1995). https://www.iso.org/standard/50461.html
[28] Cheng, Y.,Wang, Z., Chen, X., Li, Y., Li, H., Li, H., &Wang, H. (2019). Evaluation and optimization of task-oriented measurement uncertainty for coordinate measuring machines based on geometrical product specifications. Applied Sciences, 9(1), 6. https://doi.org/10.3390/app9010006
[29] Jakubiec W., & Płowucha W. (2013). First Coordinate Measurements Uncertainty Evaluation Software Fully Consistent with the GPS Philosophy. Procedia CIRP, 10, 317–322. https://doi.org/10.1016/j.procir.2013.08.049
[30] International Organization for Standardization. (2013). Geometrical Product Specifications (GPS) – systematic errors and contributions to measurement uncertainty of length measurement due to thermal influences (ISO Standard No. ISO/TR 16015:2003). https://www.iso.org/standard/29436.html
[31] Huang, Z., Zhao, L., Li, K., Wang, H., & Zhou, T. (2019). A sampling method based on improved firefly algorithm for profile measurement of aviation engine blade. Metrology and Measurement Systems, 26(4), 757–771. https://doi.org/10.24425/mms.2019.130565
[32] Ramesh, R., Mannan, M. A., & Poo, A. N. (2000). Error compensation in machine tools. A review: part I: geometric, cutting-force induced and fixture-dependent errors. International Journal of Machine Tools and Manufacture, 40(9), 1235–1256. https://doi.org/10.1016/S0890-6955(00)00009-2
[33] International Organization for Standardization. (2004). Test conditions for numerically controlled turning machines and turning centres – Part 8: Evaluation of thermal distortions (ISO Standard No. ISO 13041-8:2004). https://www.iso.org/standard/34663.html
[34] Doytchinov, I., (2017). Alignment measurements uncertainties for large assemblies using probabilistic analysis techniques. [Doctoral dissertation, Cranfield University]. CERN Document Server. https://cds.cern.ch/record/2299206
[35] Štrbac, B., Radlovacki, V., Spasic-Jokic, V., Delic, M., & Hadžistevic, M. (2017). The difference between GUM and ISO/TC 15530-3 method to evaluate the measurement uncertainty of flatness by a CMM. MAPAN, 32(4), 251–257. https://doi.org/10.1007/s12647-017-0227-3
Go to article

Authors and Affiliations

Meirbek Mussatayev
1
Meifa Huang
1
Marat Nurtas
2
Azamat Arynov
3

  1. Guilin University of Electronic Technology, School of Mechanical & Electrical Engineering, 1 Jinji Rd, Guilin, Guangxi, 541004, China
  2. International Information Technology University, Department of Mathematical and Computer Modelling, Kazakhstan
  3. School of Engineering at Warwick University, United Kingdom
Download PDF Download RIS Download Bibtex

Abstract

Due to the characteristics of color vegetation canopy images which have multiple details and Gaussion noise interference, the adaptive mean filtering (AMF) algorithm is used to perform the denoising experiments on noised images in RGB and YUV color space. Based on the single color characteristics of color vegetation canopy images, a simplified AMF algorithm is proposed in this paper to shorten the overall running time of the denoising algorithm by simplifying the adaptive denoising processing of the component V, which contains less image details. Experimental results show that this method can effectively reduce the running time of the algorithm while maintaining a good denoising effect.

Go to article

Authors and Affiliations

C. Wang
Y. Liu
P. Wang

This page uses 'cookies'. Learn more