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2. Effect of B Content on Microstructure and Wear Resistance of Fe-Cr-C Surfacing Alloy

Author

LIU Yi, LIU Qicong, YAN Yudong, HAN Zhuorui, ZHUANG Minghui

Subjects
fe-cr-c alloy； plasma powder surfacing； b addition amount； organizational structure； wear resistance, Materials of engineering and construction. Mechanics of materials, TA401-492, Technology
Abstract

In order to further improve the wear resistance of Fe-Cr-C surfacing alloy, Fe-Cr-C wear-resistant surfacing alloy layers with different B addition amounts were prepared on the surface of Q235 steel by adjusting the amount of ferroboron powder added to the surfacing alloy powder using plasma powder surfacing technology.The effects of B addition amount on the microstructure, phase composition and wear resistance of Fe-Cr-C surfacing alloy were investigated using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), abrasive wear tests and other analysis and testing methods.Results showed that when the addition amount of B was 0, the microstructure of the surfacing alloy was composed of dendritic Fe and chrysanthemum-like (Fe, Cr)7(C, B)3＋Fe eutectic structure.With the increase of B addition amount, the volume fraction of the (Fe, Cr)7(C, B)3 hard phase in the surfacing layer increased.After the precipitation amount of the (Fe, Cr)7(C, B)3 phase reached a certain value, the primary B0.7Fe3C0.3 phase was formed.When the B addition amount was 20.0%(mass fraction), a large amount of the (Fe, Cr)2 B hard phase was precipitated.The hardness of the surfacing layer showed an overall trend of rising-steep drop-rising, and the hardness of the B-10 sample reached 63.1 HRC.The results of the abrasive wear test showed that the wear resistance of the surfacing alloy layer exhibited an trend of upward-flat- upward, and the wear resistance of the B-20 sample increased to 2.8 times that of the B-0 sample.

Published
2024
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11. CRD: Collaborative Representation Distance for Practical Anomaly Detection

Author

Han, Chao, Yan, Yudong, Han, Chao, and Yan, Yudong

Abstract

Visual defect detection plays an important role in intelligent industry. Patch based methods consider visual images as a collection of image patches according to positions, which have stronger discriminative ability for small defects in products, e.g. scratches on pills. However, the nearest neighbor search for the query image and the stored patches will occupy $O(n)$ complexity in terms of time and space requirements, posing strict challenges for deployment in edge environments. In this paper, we propose an alternative approach to the distance calculation of image patches via collaborative representation models. Starting from the nearest neighbor distance with $L_0$ constraint, we relax the constraint to $L_2$ constraint and solve the distance quickly in close-formed without actually accessing the original stored collection of image patches. Furthermore, we point out that the main computational burden of this close-formed solution can be pre-computed by high-performance server before deployment. Consequently, the distance calculation on edge devices only requires a simple matrix multiplication, which is extremely lightweight and GPU-friendly. Performance on real industrial scenarios demonstrates that compared to the existing state-of-the-art methods, this distance achieves several hundred times improvement in computational efficiency with slight performance drop, while greatly reducing memory overhead.

Published
2023

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