Optimal Design of Validation Experiment for Material Deterioration.

Area of Science:

• Materials Science
• Engineering
• Computational Modeling

Background:

• Validating material deterioration models is essential for accurate simulation of real-world processes.
• Existing methods may lack cost-effectiveness or sufficient credibility.
• Standardized evaluation metrics are needed for comparing different state variables.

Purpose of the Study:

• To propose a design method for validation experiments of material deterioration models.
• To develop a cost-effective and credible experimental scheme.
• To introduce a unified metric for quantifying validation results.

Main Methods:

• Developed a normalized area metric based on probability density functions for quantitative validation.
• Utilized kernel density estimation to obtain smooth probability density functions from discrete data, reducing systematic error.
• Proposed a collaborative optimization method with Latin hypercube sampling to address varying dimensions in experimental design variables (sample number and observation moments).

Main Results:

• The proposed normalized area metric provides a unified standard for evaluating validation results across different state variables.
• The collaborative optimization method effectively solves the varying dimensional design variable problem in experiment design.
• Demonstrated the metric's characteristics and the correlation between design variables and experimental credibility through two examples.

Conclusions:

• The developed method offers a low-cost, high-credibility approach to designing validation experiments for material deterioration models.
• The normalized area metric enables robust and unified quantification of model validation.
• The optimization technique efficiently handles complex experimental design challenges.