In the mechanized pruning process of apple trees, reasonably matching cutting parameters is the key to reducing energy consumption and improving pruning quality. The conventional empirical parameter configuration usually ignores the vibration suppression effect of the branch support system, resulting in unstable cutting processes and poor cross-section quality. This study systematically investigated the influences of saw blade rotational speed, feed speed, and active support system on the sawing process of apple branches, aiming to obtain optimal operating parameters through a closed-loop research method of “simulation, optimization, and verification”. An explicit dynamic finite element model was established for multi-branch staggered sawing with three saw blades. The influence trends of each factor were analyzed via single-factor tests. A three-factor, three-level orthogonal experiment was designed based on the Box–Behnken method, and a response surface prediction model of sawing force was constructed. Regression analysis showed that the established model was extremely significant (p number of support components > saw blade rotational speed. Multi-objective optimization yielded the optimal parameter combination: rotational speed of 2500 r/min, feed speed of 2 km/h, and five support components. A prototype was manufactured according to these parameters, and field verification tests were carried out in orchards. Taking the qualified rate of cross-section quality and the missed-cut rate as evaluation indexes, the qualified rate under optimized parameters reached 95.07%, which was significantly higher than 83.11% under traditional parameters, and the missed-cut rate decreased from 11.27% to 2.63%. Results indicate that the collaborative optimization mode of “medium-high rotational speed, moderate feed speed, and active support” enables the low-vibration and high-quality sawing of apple branches. The combined method of explicit dynamics, response surface methodology, and field verification provides a systematic solution for intelligent parameter configuration of orchard pruning equipment.
Shi et al. (Tue,) studied this question.