microscope
Nowadays, how to non-destructively obtain the bending strength of the bronze-based matrix fabricated by low-temperature hot pressing sintering (HPS) is still difficult. The main contribution of our research is a proposed visual quantization model based on microstructure features of metallographic microscopy images and machine learning to predict the bending strength of bronze-based material. Exploring the interrelationship between microstructure features and mechanical properties will guide the modulation of grinding wheel composition and HPS parameters.
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In industrial microscopic detection, learning-based autofocus methods have empowered operators to acquire high-quality images quickly. Learning-based methods consist of two parts: network model and prior dataset. The network model, which approximates the relationship between input and output, exists fitting error. The prior dataset is made by sharpness metric and used for model training, while the limitations of the metric itself will affect the accuracy of the dataset.
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