Effect of Prestraining on Intergranular Corrosion Rate in Type 304 Stainless Steel in H&lt;SUB&gt;2&lt;/SUB&gt;SO&lt;SUB&gt;4&lt;/SUB&gt;-NaCl Solution

Kamide, Hidehiko; Mochida, Tsuyoshi

doi:10.2320/jinstmet1952.59.12_1253

J. Japan Inst. Metals and Materials

1995

DOI: 10.2320/jinstmet1952.59.12_1253

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Effect of Prestraining on Intergranular Corrosion Rate in Type 304 Stainless Steel in H<SUB>2</SUB>SO<SUB>4</SUB>-NaCl Solution

Hidehiko Kamide

Tsuyoshi Mochida

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“…Localized corrosion (pitting) of these composites can be explained by calculating the amplitude of the developed pits as well as their depth [1][2][3][4][5] . Localized corrosion (pitting) of these composites can be explained by calculating the amplitude of the developed pits as well as their depth [1][2][3][4][5] .…”

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confidence: 99%

Investigation of cultivated lavender (Lavandula officinalis L.) extraction and its extracts

Nadjalin

Lepojević

Ristić

et al. 2014

CI&CEQ

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Image analysis is an appropriate tool to characterize qualitatively and quantitatively the corrosion behavior in aluminum matrix composites. Localized corrosion (pitting) of these composites can be explained by calculating the amplitude of the developed pits as well as their depth 1-5 .The growth of pits in samples of an aluminum alloy reinforced with intermetallic particles in marine solutions was studied in the present contribution. Localized corrosion was assessed by cyclic polarization tests and the microstructure of corroded samples was carried out using a JEOL 6300 SEM and Visilog 5.0 image analysis software. The corroded samples were examined using both backscattered electron (BSE) and secondary electron (SE) imaging modes. The SE imaging ( Fig. 1.) allowed quantifying the dimensions of the pits that are presented on the samples. The BSE imaging (Fig. 3.) allowed determining pit depths in the cross-sectional corroded samples.We show the development of a method of image analysis that allow us: 1-Quantify the dimensions of the developed pits, Fig. 1. For these propose, we will employ the command Area of Interest Measurement (AOIM), selecting the interested area on the sample, Fig. 2. With the statistical and mathematical tools is possible to calculate the area and perimeter of the pit that corresponds to an area of 2217.90 µm 2 and a perimeter of 1004.29 µm of a total area of 512 x 368 pixels or 188416 µm 2 (calibration parameter: 51 pixels = 50 µm). We can employ the command Measure to calculate the amplitude of the pit. We calculated several diameters that include the pit (d1 = 45 µm, d2= 45 µm, d3= 47 µm, d4 = 58 µm and d5 = 50 µm) and finally the average diameter can be calculated: d = 45 µm, Fig. 2.2-Determine pit depth of corroded samples. The employed tools are basically the same, but in this case we will analyze the BSE image of a cross-section of the sample, Fig. 3. We select the pit zone and apply the AOIM command to select the area of study, Fig. 4. As a result we obtain a pit depth of 70 µm.

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Investigation of cultivated lavender (Lavandula officinalis L.) extraction and its extracts

Nadjalin

Lepojević

Ristić

et al. 2014

CI&CEQ

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“…Image analysis is an appropriate tool to characterize qualitatively and quantitatively the corrosion behavior in aluminum matrix composites. Localized corrosion (pitting) of these composites can be explained by calculating the amplitude of the developed pits as well as their depth [1][2][3][4][5] .…”

mentioning

confidence: 99%

Pit depth quantification of PM Aluminum Composites using Scanning Electron Microscopy and Image Analysis

Martínez¹,

Busquets²,

Amigó³

et al. 2002

Microsc Microanal

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Effect of Prestraining on Intergranular Corrosion Rate in Type 304 Stainless Steel in H<SUB>2</SUB>SO<SUB>4</SUB>-NaCl Solution

Cited by 2 publications

References 0 publications

Investigation of cultivated lavender (Lavandula officinalis L.) extraction and its extracts

Investigation of cultivated lavender (Lavandula officinalis L.) extraction and its extracts

Pit depth quantification of PM Aluminum Composites using Scanning Electron Microscopy and Image Analysis

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