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Surface casting defects inspection using vision system and neural network techniques

Świłło S. J., Perzyk M.

Archives of Foundry Engineering

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2013

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Vol. 13, iss. 4

103--106

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.

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Automatic inspection of surface defects in die castings after machining

Świłło S. J., Perzyk M.

Archives of Foundry Engineering

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2011

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Vol. 11, iss. 3 spec.

231--236

EN

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.

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Advanced metrology of surface defects measurement for aluminum die casting

Świłło S. J., Myszka D.

Archives of Foundry Engineering

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2011

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Vol. 11, iss. 3 spec.

227--230

EN

The scientific objective of the research is to develop a strategy to build computer based vision systems for inspection of surface defects in products, especially discontinuities which appear in castings after machining. In addition to the proposed vision inspection method the authors demonstrates the development of the advanced computer techniques based on the methods of scanning to measure topography of surface defect in offline process control. This method allow to identify a mechanism responsible for the formation of casting defects. Also, the method allow investigating if the, developed vision inspection system for identification of surface defects have been correctly implemented for an online inspection. Finally, in order to make casting samples with gas and shrinkage porosity defects type, the LGT gas meter was used . For this task a special camera for a semi-quantitative assessment of the gas content in aluminum alloy melts, using a Straube-Pfeiffer method was used. The results demonstrate that applied solution is excellent tool in preparing for various aluminum alloys the reference porosity samples, identified next by the computer inspection system.