Analiza danych pomiarowych z tomografu komputerowego na przykładzie pomiaru defektów odlewniczych

• Autorzy: Świłło S. , Myszka D. , Perzyk M.

Warianty tytułu

EN

Die casting defects measurement based on computer tomography measurement

• Języki publikacji: PL

Słowa kluczowe

PL

logistyka produkcji; procesy odlewania aluminium; tomografia komputerowa

EN

production logistics; aluminum die casting; computer tomography

• Wydawca: Sieć Badawcza Łukasiewicz - Poznański Instytut Technologiczny
• Czasopismo: Logistyka
• Rocznik: 2015
• Tom: nr 4
• Strony: 6174--6179, CD 2
• Opis fizyczny: Bibliogr. 22 poz., rys.

Twórcy

autor

Świłło S.

• Politechnika Warszawska, Wydział Inżynierii Produkcji, 02-524 Warszawa, ul. Narbutta 85, Tel: [phone]

autor

Myszka D.

• Politechnika Warszawska, Wydział Inżynierii Produkcji

autor

Perzyk M.

• Politechnika Warszawska, Wydział Inżynierii Produkcji

Bibliografia

• 1. Analysis of Casting Defects. Publisher: American Foundry Society, 3d edition, Illinois, USA, 2011
• 2. Eckart Exner H.: Stereology and 3d microscopy: useful alternatives or Competitors in the quantitative analysis of Microstructures. Image Anal Stereo, 2004, 23, p.73-82
• 3. Falęcki Z.: Analiza wad odlewów. Wydawnictwa AGH, wydanie drugie, Kraków 1997.
• 4. Filbert D., Klatte,R., Heinrich W., Purschke M.: Computer aided inspection of castings. In: IEEE-IAS Annual Meeting, Atlanta, USA (1987), p. 1087–1095
• 5. Herold F., Bavendiek K., and Grigat R.: A third generation automatic defect recognition system, Proc. 16th World Conference on Non Destructive Testing, Montreal, Canada, Aug. 30-Sep. 3, 2004.
• 6. Kyllonen J., and Pietikainen M.: Visual inspection of parquet slabs by combining color and texture, Proc. IAPR Workshop on Machine Vision Applications (MVA’00), Tokyo, Japan, 2000, p. 187-192
• 7. Luebbehuesen J.: Advanced Non-Destructive Testing by High Resolution Computed Tomography for 3D analysis of Automotive Components, GE S&IT phoenix|x-ray – Advanced NDT with hi-res CT
• 8. Mery, D., Filbert, D.: Automated flaw detection in aluminum castings based on the tracking of potential defects in a radioscopic image sequence. IEEE Trans. Robotics and Automation 18, 2002, p. 890–901
• 9. Mery D., Jaeger Th., and Filbert D.: A review of methods for automated recognition of casting defects, Insight, 44(7), 2002, p. 428-436
• 10. Mery D.: A New Approach to Detecting Defects in Aluminium Die Casting. Lecture Notes in Computer Science Vol. 2749, 2003, p. 725-732
• 11. Palanisamy S., Nagarajah C. R., Graves K., Iovenitti P.: A hybrid signal pre-processing approach in processing ultrasonic signals with noise. The International Journal of Advanced Manufacturing Technology June 2009, Vol. 42, Issue 7-8, p. 766-771
• 12. Palanisamy S, Nagarajah CR, Iovenitti P.: Effects of grain size and surface roughness on ultrasonic testing of aluminium alloy die castings. Mater Eval, 2005, 63(8), p.832–836
• 13. Perzyk M. i inni: Odlewnictwo. WNT Warszawa 2003 r.
• 14. Schulenburg H., Purschke M.: Advances in the automatic evaluation of radioscopic images. In: International Conference on Computerized Tomography for Industrial Applications and Image Processing in Radiology, March 15–17, Berlin (1999), p. 241–243
• 15. Sedgewick R., Algorithms in C, 3rd Ed., Addison-Wesley, 1998, p. 11-20
• 16. Stereology and Quantitative Metallography: A Symposium Presented at the Seventy-fourth Annual Meeting, American Society for Testing and Materials, Atlantic City, N.J., 27 June-2 July, 1971
• 17. Świłło S., Perzyk M., Automatic inspection of surface defects in die castings after machining, Archives of Foundry Engineering, Vol. 11, (3), 2011, p. 231 – 236
• 18. Świłło S., Myszka D., Advanced metrology of surface defects measurement for aluminum die casting, Archives of Foundry Engineering, Vol. 11, (3), 2011, p. 227 – 230.
• 19. Świłło S., Myszka D., Komputerowy system wizyjny do kontroli defektów odlewniczych, Eksperci NEMU, Nr 2(10), 2011 r. p. 10-12
• 20. Świłło S., Perzyk M., Automated vision system for inspection of surface casting defects based on advanced computer techniques, Supplement Proceedings, Materials Properties, Characterization and Modeling TMA (The Minerals, Metals & Materials Society) 2012 141st Annual Meeting and Exhibition, Vol. 2, 2012, p. 387-394
• 21. Świłło S., Perzyk M., Surface casting defects inspection using vision system and neural network techniques, Archives of Foundry Engineering, Vol. 13, 4/2013, p. 103-106
• 22. Xu Z., Pietikainen M., and Ojala T.: Defect classification by texture in steel surface inspection, Proc. QCAV 97 International Conference on Quality Control by Artificial Vision, Le Creusot, Burgundy, France, , May 28-30, 1997. p. 179-184