Surface casting defects inspection using vision system and neural network techniques

• Autorzy: Świłło S. J. , Perzyk M.
• Języki publikacji: EN

Abstrakty

EN

The paper presents a vision based approach and neural network techniques in surface defects inspection and categorization. Depending on part design and processing techniques, castings may develop surface discontinuities such as cracks and pores that greatly influence the material’s properties Since the human visual inspection for the surface is slow and expensive, a computer vision system is an alternative solution for the online inspection. The authors present the developed vision system uses an advanced image processing algorithm based on modified Laplacian of Gaussian edge detection method and advanced lighting system. The defect inspection algorithm consists of several parameters that allow the user to specify the sensitivity level at which he can accept the defects in the casting. In addition to the developed image processing algorithm and vision system apparatus, an advanced learning process has been developed, based on neural network techniques. Finally, as an example three groups of defects were investigated demonstrates automatic selection and categorization of the measured defects, such as blowholes, shrinkage porosity and shrinkage cavity.

Słowa kluczowe

EN

nondestructive testing; machined aluminum die castings; image processing algorithms; vision system inspection; neural network

PL

badanie nieniszczące; odlewnictwo ciśnieniowe; algorytm przetwarzania obrazu; inspekcja wizyjna; sieć neuronowa

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2013
• Tom: Vol. 13, iss. 4
• Strony: 103--106
• Opis fizyczny: Bibliogr. 15 poz., rys., tab., wykr.

Twórcy

autor

Świłło S. J.

• Institute of Manufacturing Technologies, Warsaw University of Technology, Narbutta 85, 02-524 Warsaw, Poland

autor

Perzyk M.

• Institute of Manufacturing Technologies, Warsaw University of Technology, Narbutta 85, 02-524 Warsaw, Poland

Bibliografia

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