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| Home > Publications database > Utilizing Data Processing for Enhanced Material Characterization |
| Conference Presentation (After Call) | FZJ-2025-00175 |
2024
Abstract: The main goal of material research is to improve a material‘s performance, efficiency, and longevity. To confirm these improvements and understand the underlying mechanisms, we need to characterize the material. Measuring a material‘s properties is the first step in characterization, and the raw data from the instrument gives us information about the material‘s current state. However, this raw data is often just basic, qualitative information. By processing the data, we can: 1) convert qualitative information into quantitative data, and 2) discover hidden information based on physical principles. Microscopy is a well-known tool used in material research. The data it produces is images, which are qualitative information. While humans can easily understand images, comparing them can be difficult, particularly when working with a large number of images. Data processing allows us to convert the features in the images into numerical data, making comparisons easier. This is especially valuable when dealing with large datasets, as it becomes difficult to measure the size manually. Data processing can also show trends among the experimental factors. Tensile strength is an essential property, and it‘s measured by recording the force applied to the material (stress) as it‘s stretched (strain). The data from tensile strength tests produces the familiar Stress-Strain curve, which helps us analyze the material‘s mechanical properties, such as whether it‘s ductile or brittle, and the type of failure mechanism (elastic or plastic). Single-component materials can be described as written in textbooks, but complex composite structures, with their complex interactions between structural deformation and individual component failure mechanisms, are harder to describe. Data processing allows us to correlate the structure with the material‘s mechanical behavior using the Stress-Strain curve data. Nowadays, AI (artificial intelligence) makes data processing even easier. It‘s more important to consider what information can be extracted from the raw data. By using data processing and AI, we can find hidden information in our material characterization data, helping us make new advancements in material science.
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