基于机器学习XGBoost的机制砂细粉含量预测

机制砂是由碎石或者砾石经制砂机反复破碎加工至粒径小于2.36 mm的人工砂. 在实验中把机制砂中的石粉含量和含泥量称为细粉含量, 细粉含量表征机制砂的洁净程度. 本文提出了一种基于XGBoost网络的机制砂细粉含量预测方法. 首先, 利用完全封闭的图像采集设备对机制砂细粉制成的溶液进行图像采集, 保证外界光线不会对图像拍照造成影响, 之后进行图片裁剪、读取RGB值、转LCH颜色空间等预处理, 然后构建XGBoost网络模型, 通过贝叶斯原理进行参数的循环迭代, 之后进行模型优化, 使模型的r2_score更高, 最终实现对机制砂细粉含量的预测. 结果表明: 该模型预测的数据的r2_score可以达到0.967 762, 相比于传统的多元线性回归模型、BP神经网络、传统XGBoost网络预测的r2_score为0.896 144、0.914 598、0.950 670, 预测精度有明显提高. 在实际应用中, 该方法可以缩短机制砂细粉含量测量时间, 简化机制砂细粉含量测量步骤, 是一种新型的预测机制砂细粉含量的方法.

Prediction of Fine Powder Content in Manufactured Sand Based on Machine Learning XGBoost

Manufactured sand refers to artificial sand whose particle size is less than 2.36 mm after the repeated crushing of gravels by sand-making machines. In experiments, stone powder and mud contents in the manufactured sand are called fine powder content, which represents the cleanliness of the manufactured sand. In this study, a method for predicting the fine powder content in the manufactured sand based on the XGBoost network is proposed. First, a completely closed image acquisition device is used to collect images of a solution made of fine powders in the manufactured sand, so as to guarantee that the outside light will not affect shooting. Then pre-treatment is carried out, such as picture cropping, RGB value reading, and LCH color space shifting, and an XGBoost network model is built. Through the Bayes principle, loop iteration of parameters is conducted, and the model is optimized, so as to make the r2_score of the model higher and finally predict the fine powder content in the manufactured sand. The results show that the r2_score of the data predicted by this model can reach 0.967 762. In addition, the r2_score predicted by the traditional multiple linear regression models, BP neural network, and traditional XGBoost network is 0.896 144, 0.914 598, and 0.950 670. In contrast, the prediction accuracy of the proposed model is significantly improved. In practical application, this method can shorten the measurement time and simplify the measurement steps of the fine powder content in the manufactured sand. Therefore, it is a new method for predicting the fine powder content in manufactured sand.