MAČEK, Andraž ;UREVC, Janez ;HALILOVIČ, Miroslav . Flat Specimen Shape Recognition Based on Full-Field Optical Measurements and Registration Using Mapping Error Minimization Method. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 67, n.5, p. 203-213, july 2021. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/alignment-of-modelling-and-full-field-measurement-data-for-material-characterisation-of-planar-specimens/>. Date accessed: 19 nov. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2021.7111.
Maček, A., Urevc, J., & Halilovič, M. (2021). Flat Specimen Shape Recognition Based on Full-Field Optical Measurements and Registration Using Mapping Error Minimization Method. Strojniški vestnik - Journal of Mechanical Engineering, 67(5), 203-213. doi:http://dx.doi.org/10.5545/sv-jme.2021.7111
@article{sv-jmesv-jme.2021.7111,
author = {Andraž Maček and Janez Urevc and Miroslav Halilovič},
title = {Flat Specimen Shape Recognition Based on Full-Field Optical Measurements and Registration Using Mapping Error Minimization Method},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {67},
number = {5},
year = {2021},
keywords = {full-field measurements, digital image correlation (DIC), specimen shape recognition, surface registration, iterative closest point (ICP)},
abstract = {In the paper, an alignment methodology of finite element and full-field measurement data of planar specimens is presented. The alignment procedure represents an essential part of modern material response characterisation using heterogeneous strain-field specimens. The methodology addresses both the specimen recognition from a measurement’s image and the alignment procedure and is designed to be applied on a single measurement system. This is essential for its practical application because both processes, shape recognition and alignment, must be performed only after the specimen is fully prepared for the digital image correlation (DIC) measurements (white background and black speckles) and placed into a testing machine. The specimen can be observed with a single camera or with a multi-camera system. The robustness of the alignment method is presented on a treatment of a specimen with a metamaterial-like structure and compared with the well-known iterative closest point (ICP) algorithm. The performance of the methodology is also demonstrated on a real DIC application.},
issn = {0039-2480}, pages = {203-213}, doi = {10.5545/sv-jme.2021.7111},
url = {https://www.sv-jme.eu/article/alignment-of-modelling-and-full-field-measurement-data-for-material-characterisation-of-planar-specimens/}
}
Maček, A.,Urevc, J.,Halilovič, M. 2021 July 67. Flat Specimen Shape Recognition Based on Full-Field Optical Measurements and Registration Using Mapping Error Minimization Method. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 67:5
%A Maček, Andraž %A Urevc, Janez %A Halilovič, Miroslav %D 2021 %T Flat Specimen Shape Recognition Based on Full-Field Optical Measurements and Registration Using Mapping Error Minimization Method %B 2021 %9 full-field measurements, digital image correlation (DIC), specimen shape recognition, surface registration, iterative closest point (ICP) %! Flat Specimen Shape Recognition Based on Full-Field Optical Measurements and Registration Using Mapping Error Minimization Method %K full-field measurements, digital image correlation (DIC), specimen shape recognition, surface registration, iterative closest point (ICP) %X In the paper, an alignment methodology of finite element and full-field measurement data of planar specimens is presented. The alignment procedure represents an essential part of modern material response characterisation using heterogeneous strain-field specimens. The methodology addresses both the specimen recognition from a measurement’s image and the alignment procedure and is designed to be applied on a single measurement system. This is essential for its practical application because both processes, shape recognition and alignment, must be performed only after the specimen is fully prepared for the digital image correlation (DIC) measurements (white background and black speckles) and placed into a testing machine. The specimen can be observed with a single camera or with a multi-camera system. The robustness of the alignment method is presented on a treatment of a specimen with a metamaterial-like structure and compared with the well-known iterative closest point (ICP) algorithm. The performance of the methodology is also demonstrated on a real DIC application. %U https://www.sv-jme.eu/article/alignment-of-modelling-and-full-field-measurement-data-for-material-characterisation-of-planar-specimens/ %0 Journal Article %R 10.5545/sv-jme.2021.7111 %& 203 %P 11 %J Strojniški vestnik - Journal of Mechanical Engineering %V 67 %N 5 %@ 0039-2480 %8 2021-07-08 %7 2021-07-08
Maček, Andraž, Janez Urevc, & Miroslav Halilovič. "Flat Specimen Shape Recognition Based on Full-Field Optical Measurements and Registration Using Mapping Error Minimization Method." Strojniški vestnik - Journal of Mechanical Engineering [Online], 67.5 (2021): 203-213. Web. 19 Nov. 2024
TY - JOUR AU - Maček, Andraž AU - Urevc, Janez AU - Halilovič, Miroslav PY - 2021 TI - Flat Specimen Shape Recognition Based on Full-Field Optical Measurements and Registration Using Mapping Error Minimization Method JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2021.7111 KW - full-field measurements, digital image correlation (DIC), specimen shape recognition, surface registration, iterative closest point (ICP) N2 - In the paper, an alignment methodology of finite element and full-field measurement data of planar specimens is presented. The alignment procedure represents an essential part of modern material response characterisation using heterogeneous strain-field specimens. The methodology addresses both the specimen recognition from a measurement’s image and the alignment procedure and is designed to be applied on a single measurement system. This is essential for its practical application because both processes, shape recognition and alignment, must be performed only after the specimen is fully prepared for the digital image correlation (DIC) measurements (white background and black speckles) and placed into a testing machine. The specimen can be observed with a single camera or with a multi-camera system. The robustness of the alignment method is presented on a treatment of a specimen with a metamaterial-like structure and compared with the well-known iterative closest point (ICP) algorithm. The performance of the methodology is also demonstrated on a real DIC application. UR - https://www.sv-jme.eu/article/alignment-of-modelling-and-full-field-measurement-data-for-material-characterisation-of-planar-specimens/
@article{{sv-jme}{sv-jme.2021.7111},
author = {Maček, A., Urevc, J., Halilovič, M.},
title = {Flat Specimen Shape Recognition Based on Full-Field Optical Measurements and Registration Using Mapping Error Minimization Method},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {67},
number = {5},
year = {2021},
doi = {10.5545/sv-jme.2021.7111},
url = {https://www.sv-jme.eu/article/alignment-of-modelling-and-full-field-measurement-data-for-material-characterisation-of-planar-specimens/}
}
TY - JOUR AU - Maček, Andraž AU - Urevc, Janez AU - Halilovič, Miroslav PY - 2021/07/08 TI - Flat Specimen Shape Recognition Based on Full-Field Optical Measurements and Registration Using Mapping Error Minimization Method JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 67, No 5 (2021): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2021.7111 KW - full-field measurements, digital image correlation (DIC), specimen shape recognition, surface registration, iterative closest point (ICP) N2 - In the paper, an alignment methodology of finite element and full-field measurement data of planar specimens is presented. The alignment procedure represents an essential part of modern material response characterisation using heterogeneous strain-field specimens. The methodology addresses both the specimen recognition from a measurement’s image and the alignment procedure and is designed to be applied on a single measurement system. This is essential for its practical application because both processes, shape recognition and alignment, must be performed only after the specimen is fully prepared for the digital image correlation (DIC) measurements (white background and black speckles) and placed into a testing machine. The specimen can be observed with a single camera or with a multi-camera system. The robustness of the alignment method is presented on a treatment of a specimen with a metamaterial-like structure and compared with the well-known iterative closest point (ICP) algorithm. The performance of the methodology is also demonstrated on a real DIC application. UR - https://www.sv-jme.eu/article/alignment-of-modelling-and-full-field-measurement-data-for-material-characterisation-of-planar-specimens/
Maček, Andraž, Urevc, Janez, AND Halilovič, Miroslav. "Flat Specimen Shape Recognition Based on Full-Field Optical Measurements and Registration Using Mapping Error Minimization Method" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 67 Number 5 (08 July 2021)
Strojniški vestnik - Journal of Mechanical Engineering 67(2021)5, 203-213
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
In the paper, an alignment methodology of finite element and full-field measurement data of planar specimens is presented. The alignment procedure represents an essential part of modern material response characterisation using heterogeneous strain-field specimens. The methodology addresses both the specimen recognition from a measurement’s image and the alignment procedure and is designed to be applied on a single measurement system. This is essential for its practical application because both processes, shape recognition and alignment, must be performed only after the specimen is fully prepared for the digital image correlation (DIC) measurements (white background and black speckles) and placed into a testing machine. The specimen can be observed with a single camera or with a multi-camera system. The robustness of the alignment method is presented on a treatment of a specimen with a metamaterial-like structure and compared with the well-known iterative closest point (ICP) algorithm. The performance of the methodology is also demonstrated on a real DIC application.