Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials

Papa, João Paulo; Nakamura, Ricardo; Albuquerque, Victor Hugo C. de; Falcão, Alexandre X.; Tavares, João Manuel R. S.

doi:10.1016/j.eswa.2012.07.062

Cited by 56 publications

(24 citation statements)

References 26 publications

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“…In this respect, the reported image segmentations by using the multilayer perceptron in backpropagation artificial neural network stands out, though the segmented microstructure types and their complexities were less demanding compared to present work 22–26 . The direct comparison of several machine learning classification techniques on image segmentation of graphite particles in metallurgical materials were also studied, but didn’t include the methods used here 27 .…”

Section: Introductionmentioning

confidence: 99%

Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures

Bulgarevich

Tsukamoto

Kasuya

et al. 2018

Sci Rep

132

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For advanced materials characterization, a novel and extremely effective approach of pattern recognition in optical microscopic images of steels is demonstrated. It is based on fast Random Forest statistical algorithm of machine learning for reliable and automated segmentation of typical steel microstructures. Their percentage and location areas excellently agreed between machine learning and manual examination results. The accurate microstructure pattern recognition/segmentation technique in combination with other suitable mathematical methods of image processing and analysis can help to handle the large volumes of image data in a short time for quality control and for the quest of new steels with desirable properties.

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Section: Introductionmentioning

confidence: 99%

Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures

Bulgarevich

Tsukamoto

Kasuya

et al. 2018

Sci Rep

132

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“…Em estudos de microscopia ótica são comumente usados na caracterização microestrutural [7,8,9,10] e na análise dos ferros fundidos [11,12,13,14], como nos trabalhos realizados para o cálculo da densidade de nódulos de grafita [15,16] e na quantificação de microestruturas em metais utilizando redes neurais artificiais [17].…”

Section: Figuraunclassified

Classificaçâo automatica do tipo de ferro fundido utilizando reconhecimento de padrões em imagens de microscopia

et al. 2017

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RESUMOOs ferros fundidos possuem uma microestrutura, na qual, o formato da grafita presente tem direta influência na classificação entre os diversos tipos deste material. A classificação é feita, tradicionalmente, a partir da análise visual realizada por um especialista através do auxílio de um microscópio ótico. O presente trabalho propõe a utilização da Inteligência Computacional Aplicada em conjunto com extratores de informações em imagens de metalografia. A finalidade é auxiliar profissionais da área das Ciências dos Materiais na classificação de ferros fundidos de maneira automática e reduzir o tempo de classificação, restringindo o máximo possível as falhas presentes durante a classificação. Foram realizadas duas etapas para a análise. Na primeira foram consideradas as grafitas separadamente. Em uma segunda etapa, a imagem completa foi analisada considerando todas as grafitas extraídas da mesma, em que o tipo de objeto com maior incidência na análise seria o reconhecimento adotado para toda a amostra. Em ambas as etapas o classificador Support Vector Machine obteve os melhores resultados no reconhecimento do tipo de ferro fundido, com resultados próximos a 100%, e com redução média do tempo de classificação em 92%. Tanto os resultados como o tempo das classificações são comparados com a análise do especialista, como também aos resultados obtidos em classificações do ferro fundido que utilizam uma abordagem com redes neurais e uma classificação supervisionada utilizando apenas os descritores de forma. A partir dos resultados apresentados, conclui-se que a abordagem é promissora podendo incorporar softwares comerciais para auxiliar especialistas da área. Palavras-chave:Ferro fundido, Processamento Digital de Imagens, Reconhecimento de Padrões, Inteligên-cia Computacional Aplicada. ABSTRACTThe cast iron has a microstructure in which the shape of the contained graphite has direct influence in the classification between the several types of this material. The classification is usually made through a visual analysis performed by a specialist using an optical microscope. This work proposes the use of Applied Computational Intelligence in conjunction with extractors of information in metallographic images. The purpose is to assist professionals in the field of Materials Science in the classification of cast iron automatically and reduce the time for classification, restricting as much as possible the faults presented during classification. Two steps were performed for the analysis. In the first one we considered the graphites separately. In a second step, we analyzed the complete image considering all the graphite extracted from it, in which the type of object with greater incidence in the analysis would be the recognition adopted for the whole sample. In both steps, the classifier Support Vector Machine obtained the best results in the recognition of the type of cast iron, with results close to 100%, with a mean reduction of the classification time by 92%. Both the results and the time of the classifications are ...

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“…storage modulus, loss modulus, and tan delta) with the minimal mean square error [8]. Nunes et al [9] evaluated the efficiency and accuracy of artificial intelligence techniques to classify ultrasound signals, raw data and feature selection methods, background echo and backscattered signals acquired at frequencies of 4 and 5 MHz to characterize the microstructural kinetics of phase transformations on a Nb-base alloy, thermally aged at 650 and 950°C for 10, 100 and 200 h. Papa et al [10] implemented SVMs, Bayesian and Optimum-Path Forest (OPF) based classifiers, and also the Otsu's method for automatic characterization of particles in metallographic images. De Albuquerque et al [11] presented an ANN model to automatically segment and quantify material phases from SEM metallographic images and then the results were compared to a commercial software used for quantifying material phases from metallographic images.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive mechanical property classification of vapor-grown carbon nanofiber/vinyl ester nanocomposites using support vector machines

Abuomar¹,

Nouranian²,

King

et al. 2015

Computational Materials Science

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Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials

Cited by 56 publications

References 26 publications

Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures

Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures

Classificaçâo automatica do tipo de ferro fundido utilizando reconhecimento de padrões em imagens de microscopia

Comprehensive mechanical property classification of vapor-grown carbon nanofiber/vinyl ester nanocomposites using support vector machines

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