深度学习细分与金属入侵用于硬化水泥糊的定量微结构分析
Hoan Nguyen1, Thanh-Binh Nguyen2
1Faculty of Science and Engineering, Southern Cross University, Military Rd., Lismore, NSW 2480, Australia.
概括
先进的金属入侵和深度学习细分技术成功分析了硬化水泥糊的微观结构. 这种方法准确量化了孔隙和水泥相,这对于优化复合材料性能至关重要.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 计算科学 计算科学
背景情况:
- 硬化水泥糊的定量微观结构分析对于材料性能至关重要,但由于其复杂,无序的结构而受到阻碍.
- 传统的方法很难准确地区分和量化水泥矩阵内的各种阶段.
研究的目的:
- 开发和验证一种先进的方法,用于增强水泥复合材料的微结构分析.
- 改进对硬化水泥糊中孔隙和固体相的定量评估.
主要方法:
- 在压力下注入Field金属,一个低点合金,到水泥样品中,以改善背散电子 (BSE) 成像中的相位对比度.
- 利用深度学习细分模型,特别是U-Net和LinkNet,在BSE图像中细分孔隙,无水化和水化水泥阶段.
- 在细分阶段进行量化尺寸和形状分析 (面积,直径,固度,圆形,面积比).
主要成果:
- 结合金属入和深度学习方法,可以有效地对硬化水泥中的不同阶段进行细分.
- 无论是U-Net还是LinkNet都实现了高细分精度,平均IoU分数分别为0.89和0.87.
- U-Net表现出卓越的性能,捕获更细微的细节和更复杂的边界,用于精确的相位分析.
结论:
- 这种新的方法简化了水泥复合材料微结构分析,允许对单个阶段进行详细的定量评估.
- 精确量化微结构参数对于理解材料行为和优化水泥复合材料性能至关重要.
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