Issue
Mechanics & Industry
Volume 18, Number 7, 2017
STANKIN: Innovative manufacturing methods, measurements and materials
Article Number 702
Number of page(s) 7
DOI https://doi.org/10.1051/meca/2017054
Published online 30 December 2017
  1. J.-M. Judic, Process tolerancing: a new approach to better integrate the truth of the processes in tolerance analysis and synthesis, Procedia CIRP 43 (2016) 244–249 [CrossRef] [Google Scholar]
  2. A. Azizi, Evaluation improvement of production productivity performance using statistical process control, overall equipment efficiency, and autonomous maintenance, Procedia Manuf. 2 (2015) 186–190 [CrossRef] [Google Scholar]
  3. W.A. Syed, Statistical process control for total quality, Johns Hopkins APL Tech. Dig. 13 (1992) 317–325 [Google Scholar]
  4. B. Mason, A.J. Jiju, Statistical process control: an essential ingredient for improving service and manufacturing quality, Manag. Serv. Qual. 10 (2007) 233–238 [CrossRef] [Google Scholar]
  5. P. Gejdoš, Continuous quality improvement by statistical process control, Procedia Econ. Financ. 34 (2015) 565–572 [CrossRef] [Google Scholar]
  6. D. Kiran, Total quality management. key concepts and case studies, Butterworth-Heinemann, Oxford, 2016 [Google Scholar]
  7. M. Eben-Chaime, Mutual effects of defective components in assemblies, J. Manuf. Syst. 36 (2015) 1–6 [CrossRef] [Google Scholar]
  8. N. Gaytona, P. Beaucaire, J.-M. Bourinet, E. Duc, M. Lemaire and L. Gauvrit, APTA: advanced probability-based tolerance analysis of products, Mécaniques & Industrie 12 (2011) 71–85 [CrossRef] [Google Scholar]
  9. K. Palmer, K.-L. Tsui, A review and interpretations of process capability indices, Ann. Oper. Res. 87 (1999) 31–47 [CrossRef] [Google Scholar]
  10. ISO 11462-2:2010 Guidelines for implementation of statistical process control (SPC) − Part 2: Catalogue of tools and techniques [Google Scholar]
  11. ISO 22514-2:2013 Statistical methods in process management − Capability and performance − Part 2: Process capability and performance of time-dependent process models, IDT [Google Scholar]
  12. E. Álvarez, P.J. Moya-Férnandez, F.J. Blanco-Encomienda, J.F. Muñoz, Methodological insights for industrial quality control management: the impact of various estimators of the standard deviation on the process capability index, J. King Saud Univ. Sci. 27 (2015) 271–277 [CrossRef] [Google Scholar]
  13. S. Konov, B. Markov, Algorithm of correction of error caused by perspective distortions of measuring mark images, Mechanics & Industry 17 (2016) 713 [CrossRef] [EDP Sciences] [Google Scholar]
  14. I. Smurov, M. Doubenskaia, S. Grigoriev, A. Nazarov, Optical monitoring in laser cladding of Ti6Al4V, J. Therm. Spray Technol. 21 (2012) 1357–1362 [CrossRef] [Google Scholar]
  15. ISO 7870-1:2014. Control charts − Part 1: General guidelines [Google Scholar]
  16. ISO 7870-2:2013. Control charts − Part 2: Shewhart control charts [Google Scholar]
  17. G. Capizzi, G. Masarotto, An adaptive exponentially weighted moving average control chart, Technometrics 45 (2003) 199–207 [CrossRef] [Google Scholar]
  18. J. Park, C.-H. Jun, A new multivariate EWMA control chart via multiple testing, J. Process Control 26 (2015) 51–55 [CrossRef] [Google Scholar]
  19. A. Korzenowskia, G. Vidorb, G. Vaccaroac, C. Ten Catend, Control charts for flexible and multi-variety production systems, Comput. Ind. Eng. 88 (2015) 284–292 [CrossRef] [Google Scholar]
  20. L.H. Tippett, The efficient use of gauges in quality control, Engineer 177 (1944) 481–483 [Google Scholar]
  21. A.E. Mace, The use of limit gages in process control, Ind. Qual. Control 8 (1952) 24–31 [Google Scholar]
  22. E.R. Ott, A.B. Mundel, Narrow-Limit Gaging, Ind. Qual. Control (1954) 21–28 [Google Scholar]
  23. M.I. Rozno, Process control based on narrow-limits data for attributes, Metody Menedzhmenta Kachestva 12 (2001) 27–33 [Google Scholar]
  24. F. Aparisi, E.K. Epprecht, J. Mosquera, Statistical process control based on optimum gages, Qual. Reliab. Engng. Int. (2017). (DOI:10.1002/qre.2135) [Google Scholar]
  25. S. Steiner, P. Geyer, G.O. Wesolowsky, Control charts based on grouped observations, Int. J. Prod. Res. 32 (1994) 75–91 [CrossRef] [Google Scholar]
  26. S. Steiner, P. Geyer, G.O. Wesolowsky, Shewhart control charts to detect mean and standard deviation shifts based on grouped data, Qual. Reliab. Eng. Int. 12 (1996) 345–353 [CrossRef] [Google Scholar]
  27. D.A. Masterenko, Choice of the best estimate for the measured value from strongly discretized observations, Meas. Tech. 57 (2011) 764–768 [CrossRef] [Google Scholar]
  28. D.A. Masterenko, Statistical estimation of measured quantities from strongly discretized observations with unknown scale parameter of the random component, Meas. Tech. 55 (2012) 654–658 [CrossRef] [Google Scholar]
  29. D.A. Masterenko, Increasing of precision of informational-measuring systems in automated manufacturing based on the methods of statistical processing of strongly discretized observations, Dr. Tech. Sc. Thesis, MSUT Stankin, Moscow, 2015 [Google Scholar]
  30. G. Kulldorff, Contributions to the theory of estimation from grouped and partially grouped samples, John Wiley & Sons Inc., New York, 1961 [Google Scholar]
  31. D.A. Masterenko, Advantages gained with the use of methods of statistical processing of discretized observations in coordinate measurements of intricately shaped surfaces, Meas. Tech. 58 (2015) 766–771 [CrossRef] [Google Scholar]
  32. D.A. Masterenko, V.I. Teleshevskii, Features of numerical processing of measurement information for high-precision linear and angular measurements, Meas. Tech. 59 (2017) 1254–1259 [CrossRef] [Google Scholar]
  33. L. Ren-fen, H. Deng-Yuan, On some data oriented robust estimation procedures for means, J. Appl. Stat. 30 (2003) 625–634 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.