Deep drawing is a cold forming process in which a tool forces a sheet metal flat blank into a die cavity. By definition, the depth of the drawn part is greater than the tool diameter. This technology has been extensively used in a wide range of production processes, such as, for only representative examples, those dedicated to the production of automotive fuel tanks and other parts or steel kitchen sinks. The image analysis may represent a suitable approach for the mathematical implementation of images. Such a treatment is based on the manipulation of the Gray Level image, which is firstly converted into another simplified image with a lower number of grey levels. Then, the Gray Level Co-Occurrence Matrix (GLCM) is created and finally analyzed. In the GLCM all the elements represent a correlation index among the image pixels. Given an image, the textures analysis relies on the possibility of quantifying image characteristics. Among them we have considered, as possible indicator candidates, first order parameters, such as mean, variance, skewness, and kurtosis, and second order ones, such as dissimilarity, contrast, homogeneity, angular second moment, energy, entropy, correlation.

An Application of Image Analysis to Test Samples Characterization and Wear prediction in Deep-Drawing / Belfiore, Nicola Pio; F., Ianniello; D., Stocchi; F., Casadei; I., Heikkila. - STAMPA. - (2006), pp. 259-260. ((Intervento presentato al convegno 5th Int. Conference on Mechanics and Materials in Design tenutosi a Porto - Portugal nel 24–26 July 2006.

An Application of Image Analysis to Test Samples Characterization and Wear prediction in Deep-Drawing

BELFIORE, Nicola Pio;
2006

Abstract

Deep drawing is a cold forming process in which a tool forces a sheet metal flat blank into a die cavity. By definition, the depth of the drawn part is greater than the tool diameter. This technology has been extensively used in a wide range of production processes, such as, for only representative examples, those dedicated to the production of automotive fuel tanks and other parts or steel kitchen sinks. The image analysis may represent a suitable approach for the mathematical implementation of images. Such a treatment is based on the manipulation of the Gray Level image, which is firstly converted into another simplified image with a lower number of grey levels. Then, the Gray Level Co-Occurrence Matrix (GLCM) is created and finally analyzed. In the GLCM all the elements represent a correlation index among the image pixels. Given an image, the textures analysis relies on the possibility of quantifying image characteristics. Among them we have considered, as possible indicator candidates, first order parameters, such as mean, variance, skewness, and kurtosis, and second order ones, such as dissimilarity, contrast, homogeneity, angular second moment, energy, entropy, correlation.
5th Int. Conference on Mechanics and Materials in Design
04 Pubblicazione in atti di convegno::04d Abstract in atti di convegno
An Application of Image Analysis to Test Samples Characterization and Wear prediction in Deep-Drawing / Belfiore, Nicola Pio; F., Ianniello; D., Stocchi; F., Casadei; I., Heikkila. - STAMPA. - (2006), pp. 259-260. ((Intervento presentato al convegno 5th Int. Conference on Mechanics and Materials in Design tenutosi a Porto - Portugal nel 24–26 July 2006.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/211715
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