Upcoming Work
Porosity-Advanced
Porosity-Advanced will aim to develop a cutting-edge algorithm that leverages convolutional neural networks (CNNs) to determine material porosity from microscopic images. This method will address external noise factors and inconsistencies in the imaging process, ensuring highly accurate porosity analysis even under challenging conditions. The algorithm will focus on robust feature extraction to provide reliable and precise porosity measurements for a wide range of material systems.
Learn MoreParticle Size
The Particle Size task will aim to develop a tool for analyzing microscopic images to determine the sizes of individual particles and their distribution within the sample. By processing input images in PNG or JPG format, the algorithm will extract size information and present a histogram representing particle size distribution. This will focus on analyzing small spherical particles adhered to a substrate and captured under a microscope.
Learn MoreMicrostructure Grains
The Microstructure Grains task will focus on developing a method to analyze microscopic images of material microstructures. The goal will be to determine grain size, identify phase fractions, and analyze their distribution within the microstructure. This tool will provide a deeper understanding of the material composition and structural characteristics.
Learn MoreMechanical Characteristics
The Mechanical Characteristics task will focus on developing a tool to analyze measured data in CSV format. The objective will be to plot stress-strain curves, apply smoothing techniques, and derive key mechanical properties. These include Young's modulus, yield strength, ultimate tensile strength, and the contributions of total, plastic, and elastic deformation.
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