Tensile Specimen Circular Grid Pattern and AI-Based Strain Calculation Method
Source abstract
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 preparation 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 arranged 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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Optical measurement and interference techniques · Industrial Vision Systems and Defect Detection · Advanced Numerical Analysis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Future Convergence Engineering, Kongju National University, Cheonan 31080, Republic of Korea
Department of Future Automotive Engineering, Kongju National University, Cheonan 31080, Republic of Korea
Institute of Green Car Technology, Cheonan 31080, Republic of Korea
Funding
Future Automotive Intelligent Electronics Core Technology Center, Cheonan, Republic of Korea
References (35)
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