Manufacturing Technology 2025, 25(4):511-520 | DOI: 10.21062/mft.2025.060
Strain Field Determination for Additively Manufactured Thermoplastics Using Computer Vision
- 1 Department of Applied Mechanics, Faculty of Mechanical Engineering, University of Žilina. Univerzitná 8215/1, 010 26 Žilina. Slovak Republic
- 2 Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology, Dabrowski-ego 69, 42-201 Czestochowa, Poland
The effective application of additively manufactured materials requires accurate identification of their mechanical properties as well as damage mechanisms. Computer vision offers a novel approach for non-contact measurements, enabling the identification of selected mechanical properties. This paper presents a new method based on image analysis and the detection of circular markers for non-contact displacement measurements. The core principle involves detecting the centers of gravity of the circular markers formed on the sample under investigation. The centers of gravity are evaluated on each image created during the tensile test, representing nodal points. At these points, displacements are determined based on the non-contact extensometer. The deformations sought are a function of the displacements at each nodal point. These values were calculated based on several theoretical models, also used in the finite element analysis. The paper describes the computational procedure for determining the deformations based on the mentioned theoretical models. Subsequently, the total strain field is determined using linear interpolation of the displacement values at the individual nodal points. The results provided by each of the theoretical models were compared.
Keywords: FFF, Object boundary detection, Infinitesimal strain tensor, Lagrangian strain tensor, Eulerian strain tensor
Grants and funding:
This research was funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09I03-03-V05-00002. Additional support was provided by project KEGA no. 005ŽU-4/2024
Received: July 31, 2025; Revised: October 8, 2025; Accepted: October 16, 2025; Prepublished online: October 22, 2025; Published: November 11, 2025 Show citation
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