The application of measurement information processing methods as part of modern quality control of machine-building production is examined. The task is posed and solved, of constructing procedures for measurement inspection in those cases when the deviation of a quality characteristic is described by a Rayleigh distribution. The mechanism of the generation of the Rayleigh distribution for parts of a specific type is explained. A measurement sampling plan for the single-parameter model of the distribution model is constructed. A comparative analysis is conducted of the constructed plan with the monitoring plan on the alternative criterion in accordance with ISO 2859-1, “Statistical Methods. Procedures for Sampling Inspection by an Alternative Criterion. Part 1: Plans for Sampling Inspection of Sequential Batches Based on an Acceptable Quality Level.” Monitoring statistics that make it possible to create a monitoring plan for a two-parameter model are examined. The results obtained can be useful for composing sampling monitoring plans on the quantitative criterion of batches of parts whose geometric parameters have a Rayleigh distribution, which is due to the design features and the manufacturing technology of these parts.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
S. N. Grigoriev, D. A. Masterenko, V. I. Teleshevskii, and P. N. Emelyanov, Measur. Techn., 55, No. 11, 1311–1315 (2013), https://doi.org/https://doi.org/10.1007/S11018-013-0126-0.
V. I. Teleshevskii, “Measuring informatics in mechanical engineering,” Vest. MSTU STANKIN, No. 1, 33–38 (2008).
S. N. Grigoriev, M. S. Migranov, S. R. Shekhtman, et al., SPIE Fut. Sens. Technol., 119–141 (2021), https://doi.org/https://doi.org/10.1117/12.2605753.
S. N. Grigoriev and V. I. Teleshevskii, Measur. Techn., 54, No. 7, 744–749 (2011), https://doi.org/https://doi.org/10.1007/S11018-011-9798-5.
S. N. Grigoriev, V. A. Sinopalnikov, M. V. Tereshin, and V. D. Gurin, Measur. Techn., 55, No. 5, 555–558 (2012), https://doi.org/https://doi.org/10.1007/Sl1018-012-9999-6.
S. N. Grigoriev, V. D. Gurin, M. A. Volosova, and N. Y. Cherkasova, Materwiss. Werksttech., No. 44, 790–796 (2013), https://doi.org/https://doi.org/10.1002/Mawe.201300068.
S. N. Grigoriev and G. M. Martinov, Proc. CIRP, No. 14, 517–522 (2014), https://doi.org/https://doi.org/10.1016/J.Procir.2014.03.051.
S. N. Grigoriev and G. M. Martinov, Proc. CIRP, 41, 858–863 (2016), https://doi.org/https://doi.org/10.1016/J.Procir.2015.08.031.
S. N. Grigoriev, M. P. Kozochkin, F. S. Sabirov, and A. A. Kutin, Proc. CIRP, 1, 599–604 (2012), https://doi.org/https://doi.org/10.1016/J.Procir.2012.05.006.
V. I. Leun, F. V. Nikolava, and A. V. Tignibidin, “The trend in the development of theory and practice of creating control devices to improve the accuracy and productivity of grinding operations of parts and tools in technological processes of mechanical engineering, instrumentation and tool production,” Dinam. Sist., Mekh. Mashin, No. 2, 251–254 (2012).
W. E. Deming, Out of the Crisis, MIT Press (2000), reprint ed.
Yu. P. Adler and V. I. Shper, Practical Guide to Statistical Process Management, Alpina Publ., Moscow (2019).
D. J. Wheeler and D. S. Chambers, Understanding Statistical Process Control, Addison-Wesley, (1990).
V. A. Lapidus, Shewhart’s System, Prioritet, N. Novgorod (2004).
M. I. Rozno, “Statistical quality control of products on an alternative basis with a modified tolerance (AKUD method),” Nadezhn. Kontr. Kach., No. 2, 44–52 (1992).
M. I. Rozno, “Regulation of processes based on data on an alternative feature by a narrowed tolerance,” Met. Menedzh. Kach., No. 12, 27–33 (2001).
V. G. Grigorovich, N. O. Kozlova, and S. V. Yudin, “Method of estimating the size grouping center in production conditions at ARL,” Automation of Technological Processes in Mechanical Engineering, Volgograd State Technical University (1995), pp. 197–202.
V. G. Grigorovich, N. O. Kozlova, and S. V. Yudin, “Information and statistical methods of regulation of technological processes,” Kuzn.-Stamp. Proizv., No. 9, 27–29 (2000).
H. Steiners, P. I. Geyer, and G. O. Wesolowsky, Int. J. Prod. Res., 32, No. 1, 75–91 (1994), https://doi.org/https://doi.org/10.1080/00207549408956917.
H. Steiners, P. I. Geyer, and G. O. Wesolowsky, Qual. Reliab. Eng. Int., 12, No. 5, 345–353 (1996), https://doi.org/https://doi.org/10.1002/(SICI)1099-1638(199609)12:5045::AID-QRE11>3.0.CO;2-M.
D. A. Masterenko and A. S. Metel, Mech. & Industry, 18, No. 7, 702 (2017), https://doi.org/https://doi.org/10.1051/meca/2017054.
A. I. Orlov, Applied Statistics, Ekzamen, Moscow (2004).
S. M. Raza, M. M. Butt, and A. F. Siddiqi, “Shewhart control charts for Rayleigh distribution in the presence of type I censored data,” J. ISOSS, 2, No. 2, 210–217 (2016).
Yu. A. Rozanov, Probability Theory, Random Processes and Mathematical Statistics, Nauka, Moscow (1985).
Author information
Authors and Affiliations
Corresponding author
Additional information
Translated from Izmeritel’naya Tekhnika, No. 6, pp. 28–35, June, 2022.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Grigoriev, S.N., Emelianov, P.N., Masterenko, D.A. et al. Sampling by Variables for Rayleigh Distributed Lots. Meas Tech 65, 417–425 (2022). https://doi.org/10.1007/s11018-022-02099-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11018-022-02099-0