TY - GEN
T1 - Automated Analysis of Phase Diagrams
AU - Kota, Bhargava Urala
AU - Nair, Rathin Radhakrishnan
AU - Setlur, Srirangaraj
AU - Dasgupta, Aparajita
AU - Broderick, Scott
AU - Govindaraju, Venu
AU - Rajan, Krishna
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - We present a study on automated analysis of phase diagrams to aid the materials science community that attempts to lay the groundwork for a large-scale, searchable, digitized database of phases of a wide variety of materials at different physical conditions and compositions. For this work, we concentrate on around 80 thermodynamic phase diagrams of binary metallic alloy systems which give phase information of alloys at varied temperatures and mixture ratios. We use image processing techniques to isolate phase boundaries and subsequently extract areas of the same phase. Simultaneously, document analysis techniques are employed to recognize and group the text used to label the phases; text present along the axes is identified so as to map image coordinates (x, y) to physical coordinates. Labels of unlabeled phases are inferred using standard rules. Once a phase diagram is thus digitized we are able to provide the phase of all materials present in our database at any given temperature and alloy mixture ratio. Using the digitized data, more complex queries may also be supported in the future. We evaluate our system by measuring the correctness of labeling of phase regions.
AB - We present a study on automated analysis of phase diagrams to aid the materials science community that attempts to lay the groundwork for a large-scale, searchable, digitized database of phases of a wide variety of materials at different physical conditions and compositions. For this work, we concentrate on around 80 thermodynamic phase diagrams of binary metallic alloy systems which give phase information of alloys at varied temperatures and mixture ratios. We use image processing techniques to isolate phase boundaries and subsequently extract areas of the same phase. Simultaneously, document analysis techniques are employed to recognize and group the text used to label the phases; text present along the axes is identified so as to map image coordinates (x, y) to physical coordinates. Labels of unlabeled phases are inferred using standard rules. Once a phase diagram is thus digitized we are able to provide the phase of all materials present in our database at any given temperature and alloy mixture ratio. Using the digitized data, more complex queries may also be supported in the future. We evaluate our system by measuring the correctness of labeling of phase regions.
KW - Graphics Understanding
KW - Image Processing
KW - Materials Informatics
KW - Phase diagrams
UR - https://www.scopus.com/pages/publications/85045280877
U2 - 10.1109/ICDAR.2017.256
DO - 10.1109/ICDAR.2017.256
M3 - Conference contribution
AN - SCOPUS:85045280877
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 17
EP - 18
BT - Proceedings - IAPR TC-10 12th International Workshop on Graphics Recognition, GREC 2017
PB - IEEE Computer Society
T2 - 12th IAPR TC-10 International Workshop on Graphics Recognition, GREC 2017
Y2 - 9 November 2017 through 10 November 2017
ER -