Prediction of paste yield stress based on three-dimensional convolutional neural networks

The rheological properties of paste are the foundation of the paste-filling process in metal mines, and paste yield stress is an important evaluation index for paste-filling technology. The change in ratio and concentration has a significant impact on the texture and appearance of paste slurry. Herein, a method for predicting the paste yield stress using three-dimensional convolutional neural networks (3D CNNs) is proposed through the development of image acquisition standards and an image acquisition device to collect image data sets based on a paste image data set. The Sobel operator is used to realize the pretreatment of paste edge detection and full size shrinking, and the paste image data set is obtained. The ten-fold cross-validation method is used to divide the data set to avoid accidental errors caused by a single random division. Based on the paste image–yield stress data set, the 3D CNNs model is used to extract the depth features and timing information on the paste. An image enhancement strategy for the histogram equalization algorithm is introduced to reduce the interference of environmental factors. The preprocessed data set is used for training and testing the 3D CNNs network model. In addition, the prediction accuracy of the yield stress model is analyzed: the convolutional attention block module is embedded into the CNN to optimize the model, and the introduction of channel attention and spatial attention enhances the ability of the model to perceive important areas in the image, which helps improve its ability to capture important information in the image and adjust the model parameters. The prediction accuracy of the model is increased from 93.26% to 98.19%, and the sample prediction error is within 20%, demonstrating the feasibility of paste yield stress prediction based on 3D CNNs. The image enhancement strategy using the histogram equalization algorithm can significantly improve the prediction accuracy of paste yield stress. The image enhancement strategy is applied to each model experiment, and the model prediction accuracy is improved by more than 3 percentage points. The developed image acquisition device and image acquisition standard can reduce the disturbance of environmental factors on image recognition and ensure the accuracy of paste yield stress prediction. Compared with the traditional paste rheological measurement method, the proposed method solves the problems of complex operation of traditional paste yield stress measurement, strong interference of external factors, and the difficulties associated with engineering sites.