Zhiyong Chang

Design and Experiments of Biomimetic Stubble Cutter

The fore claws of the nymph of Cryptotympana atrata have excellent ability to cut and dig soil. Inspired by this, we designed a biomimetic stubble cutter to reduce the cutting resistance. Reverse engineering and 3D print technology were applied to design the biomimetic stubble cutter. Two types of biomimetic corn stubble cutters with different tooth heights (5 mm and 2.5 mm) were designed. The cutting ability of biomimetic corn stubble cutters was compared to the conventional design by the quadratic regression orthogonal test. Tooth height, dip angle of cutting edge, and cutting velocity were chosen as orthogonal test factors. The biomimetic stubble cutters show lower cutting resistance than the conventional one. Cutting velocity exerts the least effect on cutting resistance, followed by tooth height and dip angle of cutting edge. Optimal combination with the least cutting resistance is tooth height of 2.5 mm and dip angle of cutting edge of 40° while the cutting resistance does not vary remarkably with cutting velocity. Test results indicate the serrated structure design as a principal factor for cutting resistance reduction. The biomimetic stubble cutter design, inspired by the soil-cutting mechanism of Cryptotympana atrata nymph, remarkably improves the performance of stubble cutter.

Nanoindentation mechanical properties and structural biomimetic models of three species of insects wings

Mimicking insect flights were used to design and develop new engineering materials. Although extensive research was done to study various aspects of flying insects. Because the detailed mechanics and underlying principles involved in insect flights remain largely unknown. A systematic study was carried on insect flights by using a combination of several advanced techniques to develop new models for the simulation and analysis of the wing membrane and veins of three types of insect wings, namely dragonfly (Pantala flavescens Fabricius), honeybee (Apis cerana cerana Fabricius) and fly (Sarcophaga carnaria Linnaeus). In order to gain insights into the flight mechanics of insects, reverse engineering methods were used to establish three-dimensional geometrical models of the membranous wings, so we can make a comparative analysis. Then nano-mechanical test of the three insect wing membranes was performed to provide experimental parameter values for mechanical models in terms of nano-hardness and elastic modulus. Finally, a computational model was established by using the finite element analysis (ANSYS) to analyze and compare the wings under a variety of simplified load regimes that are concentrated force, uniform line-load and a torque. This work opened up the possibility towards developing an engineering basis for the biomimetic design of thin solid films and 2D advanced engineering composite materials.

STABILITY PERFORMANCE OF INFLATABLE TUBE IMITATING CLANIS BILINEATA LARVA UNDER AXIAL PRESSURE

The body wall structure of Clanis bilineata larva exhibits strong stability. This characteristic prompted the development of a new inflatable tube to improve the stability under axial pressure. The...

Bacterial Infection Potato Tuber Soft Rot Disease Detection Based on Electronic Nose

Abstract Soft rot is a severe bacterial disease of potatoes, and soft rot infection can cause significant economic losses during the storage period of potatoes. In this study, potato soft rot was selected as the research object, and a type of potato tuber soft rot disease early detection method based on the electronic nose technology was proposed. An optimized bionic electronic nose gas chamber and a scientific and reasonable sampling device were designed to detect a change in volatile substances of the infected soft rot disease of potato tuber. The infection of soft rot disease in potato tuber samples was detected and identified by using the RBF NN algorithm and SVM algorithm. The results revealed that the proposed bionic electronic nose system can be utilized for early detection of potato tuber soft rot disease. Through comparison and analysis, the recognition rate using the SVM algorithm reached up to 89.7%, and the results were superior to the RBF NN algorithm.