The forecast of structure and properties of casting is based on results of computer simulation of physical processes which are carried out during the casting processes. For the effective using of simulation system it is necessary to validate mathematica-physical models describing process of casting formation and the creation of local discontinues, witch determinate the casting properties. In the paper the proposition for quantitative validation of VP system using solidification casting defects by information sources of II group (methods of NDT) was introduced. It was named the VP/RT validation (virtual prototyping/radiographic testing validation). Nowadays identification of casting defects noticeable on X-ray images bases on comparison of X-ray image of casting with relates to the ASTM. The results of this comparison are often not conclusive because based on operator's subjective assessment. In the paper the system of quantitative identification of iron casting defects on X-ray images and classification this defects to ASTM class is presented. The methods of pattern recognition and machine learning were applied.
@ARTICLE { IgnaszakPopielarskiKrawiec07,
ABSTRACT = { The forecast of structure and properties of casting is based on results of computer simulation of physical processes which are carried out during the casting processes. For the effective using of simulation system it is necessary to validate mathematica-physical models describing process of casting formation and the creation of local discontinues, witch determinate the casting properties. In the paper the proposition for quantitative validation of VP system using solidification casting defects by information sources of II group (methods of NDT) was introduced. It was named the VP/RT validation (virtual prototyping/radiographic testing validation). Nowadays identification of casting defects noticeable on X-ray images bases on comparison of X-ray image of casting with relates to the ASTM. The results of this comparison are often not conclusive because based on operator's subjective assessment. In the paper the system of quantitative identification of iron casting defects on X-ray images and classification this defects to ASTM class is presented. The methods of pattern recognition and machine learning were applied. },
AUTHOR = { Zenon Ignaszak and Pawe{\l} Popielarski and Krzysztof Krawiec },
JOURNAL = { Archives of Foundry Engineering },
NUMBER = { 4 },
PAGES = { 89-94 },
TITLE = { Contribution to quantitative identification of casting defects based on computer analysis of {X-ray} images },
VOLUME = { 7 },
YEAR = { 2007 },
}