Article
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Tensile Specimen Circular Grid Pattern and AI-Based Strain Calculation Method
Version 1
: Received: 29 July 2024 / Approved: 30 July 2024 / Online: 31 July 2024 (08:06:46 CEST)
How to cite: Yi, S.; Hyun, D.; Hong, S. Tensile Specimen Circular Grid Pattern and AI-Based Strain Calculation Method. Preprints 2024, 2024072393. https://doi.org/10.20944/preprints202407.2393.v1 Yi, S.; Hyun, D.; Hong, S. Tensile Specimen Circular Grid Pattern and AI-Based Strain Calculation Method. Preprints 2024, 2024072393. https://doi.org/10.20944/preprints202407.2393.v1
Abstract
During tensile testing of materials, strain measurement is conducted using either contact or non-contact methods. Contact methods offer high accuracy and precision but are limited by the specimen's thickness and dimensions, whereas non-contact methods minimize damage to thin specimens and allow measurements in various environments, though they require longer prep-aration and calculation times. This paper proposes a circular grid marking pattern and a strain prediction algorithm using artificial intelligence (AI), which simplifies the preparation process and allows strain prediction without additional equipment. The circular grid pattern can be ar-ranged in various configurations from 1×5 to 5×7, and a laser marker, which requires minimal time, was used to engrave the pattern on the specimen to shorten the preparation time. The AI model, trained on image-based data, enables strain calculation regardless of the specimen's gauge length and size, and allows measurement of local strain as well as gauge-length strain. The reliability of this concept was verified by applying it to tensile testing.
Keywords
Artificial intelligence (AI); Grid circle pattern; Image processing; Measurement strain; Tensile test
Subject
Engineering, Mechanical Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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