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Existing corn harvester cutting blades have problems associated with large cutting resistance, high energy consumption, and poor cut quality. Using bionics principles, a bionic blade was designed by extracting the cutting tooth profile curve of the B. horsfieldi palate. Using a double-blade cutting device testing system, a single stalk cutting performance contrast test for corn stalks obtained at harvest time was carried out. Results show that bionic blades have superior performance, demonstrated by strong cutting ability and good cut quality. Using statistical analysis of two groups of cutting test data, the average cutting force and cutting energy of bionic blades and ordinary blades were obtained as 480.24 N and 551.31 N and 3.91 J and 4.38 J, respectively. Average maximum cutting force and cutting energy consumption for the bionic blade were reduced by 12.89% and 10.73%, respectively. Variance analysis showed that both blade types had a significant effect on maximum cutting energy and cutting energy required to cut a corn stalk. This demonstrates that bionic blades have better cutting force and energy consumption reduction performance than ordinary blades.

With the application of elastic parameter test method of composite material mechanics, UTM6503 PC-controlled universal testing machine was adopted to study the characteristics of axial compressive mechanics on the xylem and the whole stalk in the paper. The test result showed that the average axial compressive elastic modulus of the ramie xylem of first crop of "Zhongzhu No.1" was 374.70 MPa and the average maximum compressive strength was 13.71 MPa, while the average axial compressive elastic modulus of whole stalk was 336.40 MPa, and its average maximum compressive strength was 13.19 MPa; there was no significant difference between the elastic modulus and the compressive strength of the ramie xylem and the whole stalk. In the stalk composition, xylem and phloem bond on the surface depending on their own adhesion strength which was not able to prevent the phloem slipping away along the surface of xylem. In the compressive test, it showed the load-bearing function of xylem more.

As an important part of rotating machinery, rolling bearing fault will lead to equipment fault, resulting in loss of property and personal safety. To overcome the deficiency of traditional methods, such as low recognition accuracy, slow diagnosis speed, and relying on manual extraction of features, a novel bearing fault diagnosis method based on inverted residual convolutional neural network embedded with squeeze-and-excitation block (SE-IRCNN) is proposed. This method adopts a lightweight concept to reduce the calculation amount significantly. The body of the model is built with inverted residual blocks to reduce the feature loss in the dimensional reduction. Squeeze-and-excitation block is embedded to recalibrate the features. The universality and robustness of the method are verified by changing the ratio of the train set and test set under two experimental datasets. Compared with the commonly used methods, SE-IRCNN has a smaller calculation amount, faster diagnosis speed, and higher accuracy.

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