Design and optimization of the height self-adjusting device for sweet potato combined harvesters

Abstract: Sweet potatoes have been one of the most favorite root vegetables in the world. However, manual harvesting cannot fully meet large-scale production in recent years, due to the high costing and labor intensity. It is a high demand for a low injury rate and high-level automation during collecting sweet potatoes in a combine harvester. In this study, the mechanical structure and control system were designed and constructed for the highly adaptive sweet potato collecting device. Full consideration was also given to the physical properties of materials, the kinematic and mechanical characteristics of the operation. A series of operations were then realized, including the self-adaptative dropping height, collecting potatoes, loading baskets, as well as automatically unloading and changing baskets using innovative potato collection. The height of the falling potato block was real-time adjusted to effectively reduce the sweet potato damage and broken skin, according to the adaptive function. The three-factor three-level Box-Benhnken test was conducted to explore the influencing factors on the sweet potato quality during the collecting operation after the single-factor test. Multiple regression equations were established to obtain the optimal combination of working parameters. The response surface analysis was carried out with the test indexes of injury rate, skin break rate, micro-break rate, and damage rate. The results showed that: The peeling rate of sweet potato depended mainly on the interactive effect between the rotational speed of the dropping device and the dropping height of the sweet potato. The missing rate of sweet potato depended on the interactive effect between the rotational speeds of the cleaning platform and the dropping device. The dropping height of the sweet potato posed a highly significant effect on the injured and peeling rate of the sweet potato. There was no significant effect of the rest factors on the test index. After multi-objective optimization of the regression model, the optimal combination of working parameters was obtained as follows: the rotation speed of the cleaning platform was 108.07 r/min, the rotation speed of the sweet potato dropping mechanism was 74.75 r/min, and the dropping height of the sweet potato was 18.15 cm. A field test was conducted to verify the optimization. The test results were as follows: The damage, peeling, micro-peeling, and missing rates of sweet potato were 0.39%, 0.54%, 22.93%, and 0.54%. Anyway, better consistence was achieved between the evaluation and prediction of the model. The findings can provide a strong reference to further design and optimize the highly adaptive harvester for the sweet potato.