Study On Maize Moisture And Bulk Density Detection Method Based On Frictional And Flowing Characteristics

Corn is an important crop in China, it plays an important role in national economic construction and various industries. Corn moisture and bulk density detection methods based on the flow characteristics and surface friction characteristics has been paid more and more attention for its simplicity, fast reacting rate and anti-interference ability.In this paper, corn moisture testing program is determined in accordance with the requirements of accuracy and timeliness, with the analysis of the existing bulk density and moisture detection method. The relationship between moisture and the flow characteristics and the relationship between moisture and the surface friction characteristics are analyzed after a large number of experiments. The analysis methods such as PANTA law, BP neural networks, regression analysis and cyclic redundancy are used to finish the corn quality detection. The main work is as follows:(1) The principles, methods and application scope of the corn moisture and bulk density detection are recounted. .Further studied the method of the vibration velocity and surface friction characteristics of corn moisture detection. Vibration through the study of the flow characteristics detection and the analysis of the relationship between moisture and the flow characteristics , the best parameters of the flow characteristics testing device is determined, that is the motor driving voltage is 10V, the opening height of baffle is 3.5cm, corn weight is 1500g. After analyzing the experimental data, it is considered that the relationship between the flow rate and moisture is inverse proportion, that is the flow rate decreasing with the increases of moisture, and the amplitude of decline is gradually reduced. Vibration through the analysis of the relationship between moisture and the surface friction characteristics, the dynamic friction coefficient formula of the corn is derived. After analyzing the experimental data, found that the impact the moisture influences the Surface friction coefficient is that it leads to the value of the surface friction coefficient decreased at first and then increased. After processing the data by SPSS, it is considered that the negative correlation between Surface friction coefficient and moisture is the most obvious when the glide angle is 22.07°, so it is the best glide angle. The experiments of the maximum static friction angle of the angles are carried out. The value of the bulk density is detected; the relationship between the bulk density and the moisture is drawn out.(2) The PANTA law is used to remove the invalid data of the flow characteristics and the surface friction characteristics. The automatic processing of data by c language programming is finished. Used BP neural network and regression analysis to establish the model of the relationship between the flow characteristics,the surface friction characteristics and the corn moisture. the precision of the regression analysis model is the best after comparing the effects of the single-factor neural network model of corn moisture, two-factor neural network model of corn moisture and regression analysis model of corn moisture .the two-factor neural network model is second, the single-factor neural network model is the worst. It can be seen from the calculation results that most of the results calculated by the two-factor neural network model is accorded with the requirement, but there is a certain error for individual results. There are some errors of the regression analysis model , however, it is more efficient and more stable. For computing speed and hardware design considerations,the regression analysis model is more practical.(3) a set of surface friction test bed based on the optical principles is designed. the vibration velocity and the opening height of baffle of the flow characteristics and bulk density detection device are improved. Surface friction characteristics test-bed include glide way, photoelectric timing circuit, DVCC test Chamber and computer. Height adjustable slide way, photoelectric timing circuit are designed. The DVCC test Chamber is programmed automatically timing and preserve the data. Flow characteristics and bulk density detection device include feed chute, mini motor, baffle, electronic Scale and Power Supply. Adjusted the driving voltage of motor and the opening height of baffle to meet the needs of the best testing conditions.(4)The system is developed by LabVIEW Software platform. Studied data acquisition and display module, data decoding and checksum module, calculation module. The process of communication parameters is initialized so that the equipments with the same intrinsic parameters according to the system communication protocol. CRC code is used to adjust checksum due to signal distortion caused by the environment in the software during data transmission; the detected moisture value can be close to the true value through continuous improvement of the system. The software reduces costs of the system department and it is simple. In addition, the data-processing is flexibility. It could complete the integration testing of moisture, bulk density.The system is used to detect corn moisture and bulk density. Practice has proved that it can relatively accurate reflect the moisture content of test corn, achieve the requirements of practical application. The system still needs to be improved, it can be used in corn processing, storage, transportation, acquisitions and other areas, not only corn moisture detection, but also can be used for corn bulk density detection, ultimately get a comprehensive the quality of corn quality. Also it will provide a method that has practical value to speed up the automation and modernization of the grain reservation and processing profession in our country.