Bending Strength Prediction of the Cu-Sn Alloy Through a Visual Quantization Model Integrated With Microstructure Characterization and Machine Learning

The multimodal properties of the grinding wheel matrix significantly impact grinding performance, while research on the interactions among these properties remains notably limited. To investigate the latent relationship between the microstructure and the bending strength of the bronze matrix, a visual quantization model based on the microstructure of the Cu-Sn alloy samples was established. The proposed model integrated a image segmentation network module, a quantitative characterization module, and a multivariate prediction module. The enhancement of the segmentation network is based on the synergistic combination of full-scale feature fusion with attention mechanism. The quantitative characterization parameters of metallographic microstructure features are proposed, and the most prominent intercorrelation between these parameters is studied from multiple dimensions. The results show that the modified image segmentation network exhibits superior performance compared to Unet, as evidenced by a 3% increase in Mean Intersection over Union (MIOU). The optimized output strategy ( ηDd -PSO-SVR) can contribute to the model’s prediction accuracy of material bending strength (MSE = 23.558,R 2=0.934 ). Finally, this work shows that the microscopic information demonstrates great adaptability for the machine learning models in predicting bending strength.

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Distributed Current Source Method for Modeling of Magnetic and Eddy-Current Fields in Sensing System Design

This paper presents a distributed current source (DCS) method for modeling the eddy current (EC) fields induced in biological or non-ferrous metallic objects in two-dimensional axisymmetric and three-dimensional Cartesian coordinates. The EC fields induced in the objects, magnetic flux density (MFD) in space, and magnetic flux (MF) of the sensing coils are formulated in state-space representation. The harmonic responses of the eddy current fields and electromotive force (EMF) of the sensing coil are formulated in closed-form solutions. The proposed DCS method is applied to design two eddy current sensing systems. The Bio-Differential Eddy Current (BD-EC) sensor distinguishes biological objects, and the Metal-Coaxial Eddy Current (MC-EC) sensor classifies non-ferrous metallic objects. The simulated EC field and EMF are numerically verified by comparing results with finite element analysis. An example is utilized to illustrate the advantage of the DCS method for calculating the MFD, MF, and EMF contributed from the induced ECD in the objects directly, and the EMF generated from each material. The proposed method, along with a prototype of the BD-EC sensor, has been experimentally evaluated on sweep frequency analysis for detecting meat and bone.

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