Identification of fuzzy boundaries of cutter power using control chart method
https://doi.org/10.12737/19685
Abstract
The body of knowledge of the intelligent control system e-MindMachine of the multi-operation machine contains each tool condition data including its remaining life time. This is required to obtain conclusions on the possibility of the planned machining pass, the wear size value, the necessity to change cutting conditions during the operation in the fuzzy boundary strip area, etc. In addition to the previously described methods of assessing the initial and final values of the boundary strip time as an uncertainty state area, a visual method using a modification of Shewhart control charts is offered. The adaptive control charts allow tracking the tool wear progress warning of deviations from the requirements to the process. A program sheet with the appropriate control charts and examples of their usage for the assessing the tool state and forecasting the boundary strip parameters is presented. The dependences of estimations of the expectation of the wear size and scattering within the linear tool wear zone, appropriate velocity functions and a posteriori values for the future operation in the boundary strip area are described. On this basis, the timing estimation of the boundary strip head and end is predicted. The processing of the statistical control charts is performed by the MATLABStatisticstoolbox application package.
About the Authors
Andrey K. Tugengold
Don State Technical University
Russian Federation
Russian Federation
Andrey I. Izyumov
Don State Technical University
Russian Federation
Russian Federation
DmitryY. . Terekhov
Don State Technical University
Russian Federation
Russian Federation
References
1. Vallejo, A. J. On-line Cutting Tool Condition Monitoring in Machining Processes using Artificial Intelligence. Robotics, Automation and Control, I-Tech, Vienna, Austria. 2008, 494 p.
2. Bagci, E. Monitoring and analysis of MRR-based feedrate optimization approach and effects of cutting conditions using acoustic sound pressure level in free-form surface milling. Scientific Research and Essays, 2011, vol. 6(2), pp. 256–277.
3. Grigoryev, S.N., Gurin, V.D., Cherkasova, N.Y. Povyshenie proizvoditel'nosti frezerovaniya s pomoshch'yu diagnostirovaniya sostoyaniya instrumenta s uchetom dostovernosti otobrazheniya sostoyaniya ob''ekta po kriteriyu ego otkaza. [Increasing milling performance with tool condition diagnostics taking into account validation of the object state display by its failure criterion.] Vestnik MSTU “STANKIN”, 2011, no. 3 (15), pp. 44–48 (in Russian).
4. Tugengold, А.К., Izyumov, A.I. Printsipy kontseptual'nogo podkhoda k sozdaniyu podsistemy «INSTRUMENT» v smart-pasporte mnogooperatsionnogo stanka. [Principles of conceptual approach to creating TOOL subsystem for multioperation machine smart-passport.] Vestnik of DSTU, 2014, vol. 14, no. 2, pp. 74–83 (in Russian).
5. Borisova, L.V., Nurutdinova, I.N., Dimitrov, V.P. O metodike predstavleniya nechetkikh ekspertnykh znaniy. [Оn technique of fuzzy expert knowledge representation.] Vestnik of DSTU, 2014, vol. 14, no. 4, pp. 93–102 (in Russian).
6. Tugengold, А.К., Voloshin, R.N., Yushchenko, S.V. Модуль E-MIND MACHINE в интеллектуальной системе мониторинга станка / [Module E-MIND MACHINE in the intellectual monitoring system of the machine.] International Research Journal, 2015, no. 9(40), pp. 100–102 (in Russian).
7. Tugengold, А.К., Shuchev, K.G., Izyumov, A.I., Terekhov, D.Y. Otsenka nechetkoy granitsy stoykosti instrumenta mnogooperatsionnogo stanka. [The estimation of fuzzy boundary of multioperational machine tool life.] Vestnik of DSTU, 2015, vol. 15, no. 2(81), pp. 33–41 (in Russian).
8. Solomin, S.I. Metod kontrol'nykh kart. [Control chart method.] Ekaterinburg, 2014. Available at: http://www.db.inforeg.ru/Inet/GetEzineByID/302964 (accessed: 19.01.16).
9. GOST R 50779.43–99.Statisticheskie metody. Priemochnye kontrol'nye karty. [GOST R 50779.43–99. Statistical methods. Acceptance control charts.] Moscow: Izdatel'stvo standartov, 2003, 28 p. (in Russian).
10. GOST R ISO 7870–1–2011. Statisticheskie metody. Kontrol'nye karty. Chast' 1. Obshchie printsipy. [GOST R ISO 7870–1–2011. Statistical methods. Control charts. Part 1. General guidelines.] Moscow: Standartinform, 2012, 20 p. (in Russian).
11. GOST R 50779.30–95.Statisticheskie metody. Priemochnyy kontrol' kachestva. [GOST R 50779.30–95. Statistical methods. Acceptance sampling. General requirements.] Moscow: Izdatel'stvo standartov, 2000, 23 p. (in Russian).
12. ISO3534–1–931. GOST R 50779.41–96 (ISO 7873–93).Statisticheskie metody. Kontrol'nye karty dlya arifmeticheskogo srednego s preduprezhdayushchimi granitsami. [ISO3534–1–931. GOST R 50779.41–96 (ISO 7873–93).Statistical methods. Control charts for arithmetic average with warning limits.] Moscow: Izdatel'stvo standartov, 2004, 16 p. (in Russian).
13. GOST R 50779.42–99.Statisticheskie metody. Kontrol'nye karty Shukharta. [GOST R 50779.42–99. Statistical methods. Shewhart control charts.] Moscow: Standartinform, 2008, 36 p. (in Russian).
14. GOST R 50779.43–2001. Statisticheskie metody. Priemochnye kontrol'nye karty. [GOST R 50779.43–2001. Statistical methods. Acceptance control charts.] Moscow: Standartinform, 2007, 28 p. (in Russian).
15. GOST R ISO 11462–1–2007. Statisticheskie metody. Rukovodstvo po vnedreniyu statisticheskogo upravleniya protsessami. [GOST R ISO 11462–1–2007. Guidelines for implementation of statistical process control (SPC.) Part 1: Elements of SPC (IDT).] Moscow: Standartinform, 2007, 20 p. (in Russian).
Review
For citations:
Tugengold A.K., Izyumov A.I., Terekhov D. Identification of fuzzy boundaries of cutter power using control chart method. Vestnik of Don State Technical University. 2016;16(2):43-50. (In Russ.) https://doi.org/10.12737/19685
Views:
489
Email this article 