UHCSDB: UltraHigh Carbon Steel Micrograph DataBase
Source abstract
Abstract
We present a new microstructure dataset consisting of ultrahigh carbon steel (UHCS) micrographs taken over a range of length scales under systematically varied heat treatments. Using the UHCS dataset as a case study, we develop a set of visualization tools for interacting with and exploring large microstructure and metadata datasets. Based on generic microstructure representations adapted from the field of computer vision, these tools enable image-based microstructure retrieval, as well as spatial maps of both microstructure and related metadata, such as processing conditions or properties measurements. We provide the microstructure image data, processing metadata, and source code for these microstructure exploration tools. The UHCS dataset is intended as a community resource for development and evaluation of microstructure data science techniques and for creation of microstructure data science teaching modules.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Machine Learning in Materials Science · Mineral Processing and Grinding · Cultural Heritage Materials Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (23)
Show source-provided references
1. Hecht MD, DeCost BL, Francis T, Holm EA, Picard YN, Webler BA Ultrahigh carbon steel micrographs. https://hdl.handle.net/11256/940
2. Hecht MD, Webler BA, Picard YN (2016) Digital image analysis to quantify carbide networks in ultrahigh carbon steels. Mater Charact 117:134–14310.1016/j.matchar.2016.04.012 ↗
3. HechtMD, Picard YN,Webler BA (2017) Coarsening of inter- and intra-granular proeutectoid cementite in an initially pearlitic 2c-4cr ultrahigh carbon steel. Metall and Mater Trans A 48(5):2320–233510.1007/s11661-017-4012-2 ↗
4. DeCost BL, Francis T, Holm EA (2017) Exploring the microstructure manifold: image texture representations applied to ultrahigh carbon steel microstructures. Acta Materialia10.1016/j.actamat.2017.05.014 ↗
5. DeCost BL, Francis T, Holm EA UHCSDB microstructure explorer. http://uhcsdb.materials.cmu.edu . Accessed 14 April 2017
6. Smith R (2007) An overview of the tesseract ocr engine 9th International Conference on Document Analysis and Recognition, 2007. ICDAR 2007, vol 2. IEEE, pp 629–633
7. O’Mara J, Meredig B, Michel K (2016) Materials data infrastructure: a case study of the citrination platform to examine data import, storage, and access. JOM 68(8):2031–203410.1007/s11837-016-1984-0 ↗
8. Michel K, Meredig B (2016) Beyond bulk single crystals: a data format for all materials structure–property–processing relationships. MRS Bull 41(8):617–62310.1557/mrs.2016.166 ↗
9. Puchala B, Tarcea G, Marquis EA, Hedstrom M, Jagadish HV, Allison JE (2016) The materials commons: a collaboration platform and information repository for the global materials community. JOM 68(8):2035–204410.1007/s11837-016-1998-7 ↗
10. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–44410.1038/nature14539 ↗
11. Van Der Maaten L, Postma E, Van den Herik J (2009) Dimensionality reduction: a comparative review. J Mach Learn Res 10:66–71
12. van der Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9:2579–2605. (Nov)
13. Jolliffe IT (2002) Principal component analysis, 2nd ed, Springer, New York
14. Kruskal JB (1964) Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29(1):1–2710.1007/BF02289565 ↗
15. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290 (5500):2323–232610.1126/science.290.5500.2323 ↗
16. Tenenbaum JB, De Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–232310.1126/science.290.5500.2319 ↗
17. Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–139610.1162/089976603321780317 ↗
18. NIST repositories https://materialsdata.nist.gov . Accessed 14 April 2017
19. Hill J, Mulholland G, Persson K, Seshadri R, Wolverton C, Meredig B (2016) Materials science with large-scale data and informatics: Unlocking new opportunities. MRS Bull 41(05):399–40910.1557/mrs.2016.93 ↗
20. Seshadri R, Sparks TD (2016) Perspective: Interactive material property databases through aggregation of literature data. APL Materials 4(5):05320610.1063/1.4944682 ↗
21. Chollet F (2015) Keras https://github.com/fchollet/keras. Accessed 22 May 2017
22. Van der Walt S, Schönberger J L, Nunez-Iglesias J, Boulogne F, Warner J D, Yager N, Gouillart E, Yu T (2014) scikit-image: image processing in python. PeerJ e453:210.7717/peerj.453 ↗
23. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12:2825–2830