Design and experimental study on pruning machine of Yunnan edible rose

Abstract

Edible rose is one of the main cash crops in Yunnan, China. Due to the high degree of lignification of rose stalks which is difficult to cut, and roses can only be pruned by hand after picking. Most of Yunnan Province is hilly landscape. Therefore, it is necessary to design an efficient small rose pruner for hills. Based on the experimental results of the physical and mechanical properties of rose stems, a simulation experiment of three different cutting methods was conducted to determine the optimal scheme. The results showed that single disc cutter is the best cutting method. An improved disc cutter with variable sliding angle is designed. In order to study the effects of the different structure parameters on the performance of the pruning machine and get the optimal combination of parameters, the orthogonal test was conducted. Forward speed, rotational speed of cutter, number of blades, cutting angle and knife-edge angle as factors, and the quality of pruning was selected as the evaluation index. The experiment results indicated that the working performance of machine was the highest when the number of blades was 6, rotational speed of cutter was 2000 rpm, cutting angle was 20°, knife-edge angle was 10°, forward speed was 0.5 m s−1. The average quality of pruning index, miss cutting index and tearing index were 90.6%, 5.68% and 3.72%, respectively. The designed machine can meet the agronomic requirements of Yunnan edible rose pruning operation. The research provides a reference for the design and extending of the pruning machine for edible rose.

Design and Experimental Study of Horizontal-Shaft Roller-Type Cotton Stalk Puller Based on Response Surface Method

“…The Box-Behnken central composite design method was adopted in the test, and a three-factor threelevel quadratic regression orthogonal test scheme was used (Bano and Irfan, 2019), followed by analysis through the response surface method (Oznur et al, 2023;Zhang and Wu, 2023). Moreover, a mathematical model indicating the relationship between pulling force and each factor was established to obtain the optimal combination of working parameters (Zhu et al, 2023). The test factors and levels are listed in Table 3.…”