Name
Abstract | ||
|---|---|---|
This study focuses on the development of a code to perform an appropriate analysis of nodular cast iron metallography. The platform developed was written in Python programming language and used the Open Source Computer Vision library (OpenCV) for the image processing. The OpenCV tool was applied in order to convert the color photo of the metallography to a grayscale image and hence enable the segmentation of the gray phases to calculate the percentage of carbon within the cast iron test specimen. The categorized microstructural phases were perlite, ferrite and graphite. For validation of the platform and the methodology, the obtained results were contrasted with the Architecture Street Furniture (ASF) ductile iron chart, from there the percentage of differences between the model developed and the baseline specimens were among 2 to 18% for ferrite, 0.4 to 2.2% for pearlite and 2.1 to 12.1% for graphite. Furthermore, the obtained nodularity from the study cases were compared using examples from the ASTM A247 norm and the differences were between 1.4 to 8.1%. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-20040-4_30 | ADVANCES IN HUMAN FACTORS AND SYSTEMS INTERACTION |
Keywords | DocType | Volume |
Nodular cast iron, Digital image processing, Python, OpenCV, Nodularity | Conference | 959 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Victor Hidalgo | 1 | 0 | 0.34 |
| Carlos Díaz | 2 | 0 | 0.34 |
| Anibal Silva | 3 | 0 | 0.34 |
| José Erazo | 4 | 0 | 0.34 |
| Esteban Valencia | 5 | 0 | 0.34 |