机制砂粒径粒形检测系统开发及实验研究

发布时间：2019-02-24 17:29

【摘要】：沥青混合料是目前世界上用量最大的道路建设材料,从体积构成看,沥青混合料中细集料占总体积的40%~50%。巨大的市场需求使国内天然砂资源逐年减少,机制砂替代天然砂是必然趋势。但是我国机制砂粒径、粒形质量参差不齐,机制砂形态质量检测是保证混合料质量的关键。传统振动筛分法只能实现机制砂粒径级配检测,图像法能同时实现粒径和粒形检测。本文针对粒径为0.6~4.75 mm的机制砂进行检测算法研究及开发相应的检测系统。设计机制砂振动分散系统,采用CCD相机实现对下落砂粒进行图像采集,为了减小机制砂表面颜色对检测结果的影响,设计了无影背光光源系统,研制了机制砂粒径、粒形检测硬件系统。采用高斯滤波对灰度化后的图像进行去噪;采用最大类间方差法对滤波后的图像进行分割得到二值图;利用颗粒几何特征判别图像边界处不完整颗粒,并进行去除;利用Hu矩特征识别并消除相邻图片中被重复拍摄的颗粒;通过求取颗粒凸包和凹点检测,分离了粘连的颗粒。提出了一种新的图像标定方法。基于Visual Studio C++、Open CV库和Qt开发了机制砂粒径粒形检测软件系统。对0.6~4.75 mm粒径范围的机制砂单级料、级配料分别进行了粒径、粒形检测的重复性试验、与新帕泰克QICPIC动态颗粒图像分析仪的精度对比试验。实验结果表明,对单级料和级配料的粒径检测最大重复性误差为3.46%和0.51%,粒形检测最大重复性误差为2.97%和0.85%,颗粒形状越趋于球形,重复性越好。与新帕泰克的粒径结果对比,单级料与级配料两仪器的最大偏差为7.19%和6.02%,粒形结果最大偏差为3.08%和2.42%。针对图像法和振动筛分法机制砂粒径检测的差异性,提出了一种粒径修正方法,修正后粒径检测精度能满足工程实际测量需求。以国标《公路工程集料试验规程》(JTG E42-2005)中测量细集料粒形棱角性的流动时间法为对比,对不同粒形表征参数和流动时间进行了相关性研究,得到等效椭圆长短轴比为最优粒形表征参数。所开发的检测系统能满足机制砂粒径、粒形实验室检测需求,能有效监测机制砂质量。
[Abstract]:Asphalt mixture is the most used road construction material in the world at present. In terms of volume composition, fine aggregate in asphalt mixture accounts for 40% of the total volume. The huge market demand makes the natural sand resource decrease year by year. It is inevitable to replace the natural sand with machine-made sand. However, the particle size and grain shape of machine-made sand are not uniform in China, and the quality detection of machine-made sand is the key to ensure the quality of mixture. The traditional vibrating sieve method can only detect the particle size gradation of machine-made sand, and the image method can detect the particle size and shape at the same time. In this paper, the detection algorithm of machine-made sand with a particle size of 0.6 ~ 4.75 mm is studied and the corresponding detection system is developed. The mechanism sand vibration dispersion system is designed, and the falling sand image is collected by CCD camera. In order to reduce the influence of the surface color of the machined sand on the detection result, a non-shadow backlight light source system is designed, and the particle size of the machined sand is developed. Grain shape detection hardware system. Gao Si filter is used to denoise the grayscale image; the maximum inter-class variance method is used to segment the filtered image to obtain the binary image; the incomplete particles at the edge of the image are identified by the geometric characteristics of the particles and removed. The Hu moment feature is used to identify and eliminate the repeated shot particles in the adjacent images, and the conglutinated particles are separated by detecting the convex hull and the concave point of the particles. A new image calibration method is proposed. Based on Visual Studio C, Open CV library and Qt, a software system for particle shape detection of machine-made sand is developed. The reproducibility tests of particle size and particle shape were carried out for the single grade compound of machine-made sand with the particle size range of 0.6 ~ 4.75 mm, and the precision comparison test of the new QICPIC dynamic particle image analyzer was carried out. The experimental results show that the maximum repeatability error of particle size detection is 3.46% and 0.51% for single stage and grade proportioning, and 2.97% and 0.85% for particle shape detection. The more spherical the particle shape, the better the repeatability. Compared with the results of the particle size of neopateck, the maximum deviation of the single stage material and the grade batching is 7.19% and 6.02%, and the maximum deviation of the particle shape is 3.08% and 2.42% respectively. In view of the difference between the image method and the vibrating screen method, a particle size correction method is proposed. The precision of the modified particle size measurement can meet the needs of practical engineering measurement. Based on the flow time method for measuring fine aggregate shape and angularity in JTG E42-2005, the correlation between different particle shape characterization parameters and flow time was studied, by comparing with the flow time method in the National Standard "Highway Engineering aggregate Test Code" (JTG E42-2005). The equivalent ellipse long and short axis ratio is obtained as the optimal particle shape characterization parameter. The developed testing system can meet the requirements of particle size and particle shape laboratory testing, and can effectively monitor the quality of machined sand.
【学位授予单位】：华侨大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U414;TP391.41

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