Particles and Grains Laboratory, School of Civil Engineering, The University of Sydney, Sydney, NSW, 2006, Australia.
Sci Rep. 2022 Aug 17;12(1):13915. doi: 10.1038/s41598-022-18250-4.
Sorting granular materials such as ores, coffee beans, cereals, gravels and pills is essential for applications in mineral processing, agriculture and waste recycling. Existing sorting methods are based on the detection of contrast in grain properties including size, colour, density and chemical composition. However, many grain properties cannot be directly detected in-situ, which significantly impairs sorting efficacy. We show here that a simple neural network can infer contrast in a wide range of grain properties by detecting patterns in their observable kinematics. These properties include grain size, density, stiffness, friction, dissipation and adhesion. This method of classification based on behaviour can significantly widen the range of granular materials that can be sorted. It can similarly be applied to enhance the sorting of other particulate materials including cells and droplets in microfluidic devices.
对矿石、咖啡豆、谷物、砾石和药丸等颗粒材料进行分类对于矿物加工、农业和废物回收等应用至关重要。现有的分类方法基于对包括大小、颜色、密度和化学成分在内的谷物特性差异的检测。然而，许多谷物特性不能在现场直接检测，这极大地影响了分类效果。我们在这里表明，通过检测其可观察运动学中的模式，一个简单的神经网络可以推断出广泛的谷物特性的差异。这些特性包括颗粒大小、密度、硬度、摩擦、耗散和附着力。这种基于行为的分类方法可以显著拓宽可分类的颗粒材料的范围。它同样可以应用于增强微流控装置中细胞和液滴等其他颗粒材料的分类。
