Desenvolvimento de um Software para cálculo da densidade de nódulos de grafita em ferro fundido nodular através de Processamento Digital de Imagens

Peixoto, Filipe de Macêdo; Rebouças, Elizângela de S.; Xavier, Francisco Geilson de Lima; Filho, Pedro Pedrosa Rebouças

doi:10.1590/s1517-707620150001.0026

Cited by 8 publications

(5 citation statements)

References 2 publications

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“…The Computer Vision system developed in this application follows the same methodology of a typical computer vision system, and consist of the steps of image acquisition, preprocessing, segmentation, extraction of attributes, identification of previously established standards and, finally, is generated a report with analysis and information obtained, as shown in Figure 1 [6,13,12,3]. Fig.…”

Section: Methodsmentioning

confidence: 99%

Computer Vision System to Aid Drivers of Vehicles through Vertical Signaling Traffic

̧as¹,

Gomes²,

Filho

2015

IJCA

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The constant increase in the number of vehicle, especially in large urban centers, a growing number of accidents caused by the invasion of neighboring groups of drivers, either by inattention, drowsiness, among others. To mitigate the impact of this problem, this paper proposes the Aid System the Driver Vehicle (ASDV), which is based on a system uses computer vision techniques and Digital Image Processing to detect the tracks of markings and through these identify the behavior that the car driver must have to continue in its tracks of movement on the road. Tests are performed on Unix platform and the Android operating system, obtaining the speed 50 fps and 20 fps, respectively. The tests show satisfactory results, achieving 100% accuracy when the tracks are detected. Therefore, we can conclude that the system is promising and shows potential to be used in real applications. General Terms:Vertical traffic signs, aid to the car driver, Computer Vision

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Section: Methodsmentioning

confidence: 99%

Computer Vision System to Aid Drivers of Vehicles through Vertical Signaling Traffic

̧as¹,

Gomes²,

Filho

2015

IJCA

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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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“…There is an increasing number of researches that employ computational techniques in several fields of Materials Science to assist professionals and specialists in their analytical activities. There are in the literature, studies that used computational methods to analyze the microstructure of the cast iron [7], [8], calculate the graphite density nodules [9], classify the electric steels according to the electromagnetic efficiency [10]- [12], and measures the welding dilution [13].…”

Section: Introductionmentioning

confidence: 99%

Automatic Evaluation of the Mechanical Properties of Steels Maraging using Digital Image Processing Techniques

Viana¹,

Rabelo²,

Santos³

et al. 2020

Anais Estendidos Da Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2020)

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Some strategic sectors of the economy require that the raw material of their machines and equipment have mechanical properties that satisfy their use. Maraging steel is a material of great concern since it is necessary to have a high mechanical resistance associated with high fracture toughness. The traditional tests to determine the fracture toughness of this material before use in applications are the Charpy and KIC tests. However, this process is characterized by being exhaustive and requiring specialized and trained professionals. Thus, to reverse this situation, this work proposes a new approach to determine the mechanical properties of maraging steel. For this, initially, the method removes any artifacts present in the image resulting from the mode of acquisition. In sequence, this works tested the method Extended Minimum Transformation (EMT) and mathematical morphology to find these markers of the regions of the dimples. Then, the Adaptive Thresholding, Optimal Global Thresholdusing the Otsu Method and Watershed transformation methods were used to segment the dimples. In the end, the diameter of the dimples and the toughness of the material were calculated. Tests are carried out and compared with the result obtained by specialists using the traditional system to evaluate the proposed approach. The results obtained were satisfactory for the application because the proposed approach presented speed and precision to the conventional methods.

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Desenvolvimento de um Software para cálculo da densidade de nódulos de grafita em ferro fundido nodular através de Processamento Digital de Imagens

Cited by 8 publications

References 2 publications

Computer Vision System to Aid Drivers of Vehicles through Vertical Signaling Traffic

Computer Vision System to Aid Drivers of Vehicles through Vertical Signaling Traffic

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

Automatic Evaluation of the Mechanical Properties of Steels Maraging using Digital Image Processing Techniques

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