Quantifying Alignment Deviations for the In-Plane Biaxial Test System via a Shape-Optimised Cruciform Specimen.

Area of Science:

• Materials Science
• Mechanical Engineering
• Computational Mechanics

Background:

• Loading coaxiality and specimen design critically influence in-plane biaxial test results.
• Existing methods lack robust solutions for correcting coaxiality and specimen design, leading to significant data scatter.
• Variability in test results across laboratories and tests necessitates improved quantification methods.

Purpose of the Study:

• To develop a reliable method for quantifying loading coaxiality in in-plane biaxial test systems.
• To address the data scatter issue caused by alignment deviations in biaxial material testing.
• To enhance the accuracy and reproducibility of biaxial stress tests.

Main Methods:

• Developed a model integrating strain distribution analysis with Automated Machine Learning (AutoML).
• Utilized shape-optimized cruciform specimens for accurate strain measurement.
• Quantified alignment deviations based on calculated strain distributions.

Main Results:

• The developed AutoML model successfully quantified alignment deviations.
• 99.2% of quantified errors were found to be less than 5%, indicating high accuracy.
• The method effectively solved the challenge of quantifying alignment deviations in biaxial test systems.

Conclusions:

• The developed quantification method enhances the reliability of biaxial test data.
• Improved alignment quantification aids in assembly efficiency and material failure criteria construction.
• This approach provides a robust solution for consistent and accurate in-plane biaxial material testing.