The Radar Tomography Detection for the Abnormal Moisture Regions of Huge Grain Pile

As far as water content detection of stored-grain is concerned, cross-hole radar tomography method is used to find the abnormal water content areas in granary. Based on the basic theory of tomography, wave front ray tracing methods have been studied. Travel-time equation is used to correct refraction points before ray tracing, which improves the precision of ray tracing, and is also easy to realize. Velocity and attenuation tomography forward model were built respectively, and Least Square QR-factorization (LQSR) image rebuild methods were adopted to solve the inversion equation. Meanwhile, the properties of the two ray tracing methods were analyzed. Simulation and experimental results show that, compared to traditional methods, there is a better performance for the improved ray tracing proposed in this paper. It's proved that using cross-hole radar tomography method to detect the internal structure of huge granary is feasible

Prediction Model for the Stored-Grain Situation Risk Point Based on Broad Learning Network

An accurate prediction for the stored-grain situation is a necessary means to ensure grain security. It is difficult for traditional machine learning methods to process large-scale stored-grain monitoring data, while deep learning methods have the problems of difficult model training and high resource consumption. To solve these problems, a risk prediction model for stored-grain situations was proposed based on a broad learning network. First, the concept of stored-grain risks was proposed and defined in three categories: low, medium, and high. Then, based on the existing broad learning network, two improved broad learning algorithms were proposed: an enhanced node incremental algorithm and an input data incremental algorithm. Based on the multi-modal features of stored-grain situation data, canonical correlation analysis is introduced to the feature extraction and fusion methods. Finally, an accurate prediction model of stored-grain risk based on improved broad learning and correlation analysis is constructed. The experimental results show that, compared to the traditional broad learning model, the two improved broad learning models improve the accuracy of stored-grain risk prediction by more than 2.3%. Meanwhile, compared with the existing deep learning models, the improved broad learning model reduces training time by more than 20 times without reducing the prediction accuracy and has better robustness. In short, the incremental learning training method can make the prediction accuracy of the broad learning model close to or reach the level of the deep learning model with the increase of training data, which proved that the proposed methods may be an effective alternative to deep learning models

Three-Dimensional Numerical Simulation of Grain Growth during Selective Laser Melting of 316L Stainless Steel

The grain structure of the selective laser melting additive manufactured parts has been shown to be heterogeneous and spatially non-uniform compared to the traditional manufacturing process. However, the complex formation mechanism of these unique grain structures is hard to reveal using the experimental method alone. In this study, we presented a high-fidelity 3D numerical model to address the grain growth mechanisms during the selective laser melting of 316 stainless steel, including two heating modes, i.e., conduction mode and keyhole mode melting. In the numerical model, the powder-scale thermo-fluid dynamics are simulated using the finite volume method with the volume of fluid method. At the same time, the grain structure evolution is sequentially predicted by the cellular automaton method with the predicted temperature field and the as-melted powder bed configuration as input. The simulation results agree well with the experimental data available in the literature. The influence of the process parameters and the keyhole and keyhole-induced void on grain structure formation are addressed in detail. The findings of this study are helpful to the optimization of process parameters for tailoring the microstructure of fabricated parts with expected mechanical properties

Characterization of Wheat Varieties Using Terahertz Time-Domain Spectroscopy

Terahertz (THz) spectroscopy and multivariate data analysis were explored to discriminate eight wheat varieties. The absorption spectra were measured using THz time-domain spectroscopy from 0.2 to 2.0 THz. Using partial least squares (PLS), a regression model for discriminating wheat varieties was developed. The coefficient of correlation in cross validation (R) and root-mean-square error of cross validation (RMSECV) were 0.985 and 1.162, respectively. In addition, interval PLS was applied to optimize the models by selecting the most appropriate regions in the spectra, improving the prediction accuracy (R = 0.992 and RMSECV = 0.967). Results demonstrate that THz spectroscopy combined with multivariate analysis can provide rapid, nondestructive discrimination of wheat varieties

Grain growth prediction in selective electron beam melting of Ti-6Al-4V with a cellular automaton method

10.1016/j.matdes.2020.109410Materials and Design19910941