- Tytuł:
- Automatic inspection of surface defects in die castings after machining
- Autorzy:
-
Świłło, S. J.
Perzyk, M. - Powiązania:
- https://bibliotekanauki.pl/articles/380799.pdf
- Data publikacji:
- 2011
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
nondestructive testing
casting defect
machined aluminum
die casting
image processing algorithm
vision system inspection
badania nieniszczące
wada odlewu
odlewanie ciśnieniowe
algorytm przetwarzania obrazu
inspekcja wizyjna - Opis:
- A new camera based machine vision system for the automatic inspection of surface defects in aluminum die casting was developed by the authors. The problem of surface defects in aluminum die casting is widespread throughout the foundry industry and their detection is o f paramount importance in maintaining product quality. The casting surfaces are the most highly loaded regions of materials and components. Mechanical and thermal loads as well as corrosion or irradiation attacks are directed primarily at the surface of the castings. Depending on part design and processing techniques, castings may develop surface discontinuities such as cracks or tears, inclusions due to chemical reactions or foreign material in the molten metal, and pores that greatly influence the material ability to withs tand these loads. Surface defects may act as a stress concentrator initiating a fracture point. If a pressure is applied in this area, the casting can fracture. The human visual system is well adapted to perform in areas of variety and change; the visual inspection processes, on the other hand, require observing the same type of image repeatedly to detect anomalies. Slow, expensive, erratic inspection usually is the result. Computer based visual inspection provides a viable alternative to human inspectors. Developed by authors machine vision system uses an image processing algorithm based on modified Laplacian of Gaussian edge detection method to detect defects with different sizes and shapes. The defect inspection algorithm consists of three parameters. One is a parameter of defects sensitivity, the second parameter is a thres hold level and the third parameter is to identify the detected defects size and shape. The machine vision system has been successfully tested for the different types of defects on the surface of castings.
- Źródło:
-
Archives of Foundry Engineering; 2011, 11, 3 spec.; 231-236
1897-3310
2299-2944 - Pojawia się w:
- Archives of Foundry Engineering
- Dostawca treści:
- Biblioteka Nauki
Artykuł