Vibratory threshing mechanism and multi-species threshing parameter testing of wine grapes based on discrete element method

IF 8.9 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture Pub Date : 2025-09-15 DOI:10.1016/j.compag.2025.110955

Huineng Zhou, Haochao Tan, Congcong Shen, Junlong Ma, Zhaoyang Guo, Zhendong Huang, Liming Xu, Shuai Ma

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引用次数: 0

Abstract

This study aimed to achieve precision harvesting of wine grapes by investigating their vibratory threshing mechanism and measuring key harvesting parameters across eight varieties at different maturity stages. This study measured the geometric and physical parameters as well as the mechanical properties of Marselan bunches. The average values of the fruit detachment force (FDF) and fruit bursting force (FBF) at the optimal ripeness stage were found to be 3.22 N and 8.52 N, respectively. Based on the measurement results, a discrete element flexible model of grape bunches was established using the Bonding V2 Model. A vibration-based threshing parameter testing system for wine grapes was developed, featuring adjustable vibration frequency, angular amplitude, and movement speed, as well as functionality for detecting the vibration intensity of grape bunches. The accuracy of the simulation model was verified by comparing results with bench tests. A multifactor simulation test was conducted on Marselan grapes at the harvestable stage, using a Box-Behnken Design (BBD). The experimental factors included frequency, angular amplitude, movement speed, and sugar content, with threshing rate and breakage rate as response variables. The relative errors between simulation and bench tests were 2.36 % for threshing rate, 7.86 % for breakage rate, and 10.3 % for maximum acceleration amplitude. Analysis revealed two primary forms of berry threshing: impact threshing form, which predominantly affects the breakage rate, and vibratory threshing form, which primarily influences the threshing rate. It was identified that operational parameters (frequency, angular amplitude, and movement speed) and inherent varietal characteristics (FDF, FBF, and berry mass) were key factors affecting threshing quality. Furthermore, the relationships between these parameters and the threshing rate and breakage rate were clarified. The coefficient of breakage (CB) was introduced as an indicator of grape suitability for vibratory threshing for the first time. Grapes with a CB below 0.1 were considered unsuitable for mechanical threshing. Finally, orthogonal tests were conducted to identify the optimal combination of threshing parameters for several varieties at different maturity levels. The results provide a reference for optimizing the mechanized harvesting of wine grapes.

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基于离散元法的酿酒葡萄振动脱粒机理及多品种脱粒参数测试

本研究通过对8个不同成熟期的酿酒葡萄振动脱粒机理的研究和关键采收参数的测定，旨在实现酿酒葡萄的精准采收。本研究测量了马塞兰束的几何和物理参数以及力学性能。最佳成熟期果实剥离力（FDF）和破裂力（FBF）的平均值分别为3.22 N和8.52 N。基于实测结果，利用Bonding V2模型建立了葡萄串的离散元柔性模型。研制了一种基于振动的酿酒葡萄脱粒参数检测系统，该系统具有振动频率、角振幅、运动速度可调，并具有检测葡萄串振动强度的功能。通过与台架试验结果的比较，验证了仿真模型的准确性。采用Box-Behnken设计（BBD）对可收获期的马塞兰葡萄进行了多因素模拟试验。以脱粒率和破碎率为响应变量，试验因素包括频率、角幅、运动速度和含糖量。脱粒率、破碎率和最大加速度幅值的相对误差分别为2.36%、7.86%和10.3%。分析表明，浆果脱粒主要有两种形式：冲击脱粒形式和振动脱粒形式，前者主要影响破碎率，后者主要影响脱粒率。结果表明，操作参数（频率、角振幅和移动速度）和品种内在特性（FDF、FBF和浆果质量）是影响脱粒质量的关键因素。进一步阐明了这些参数与脱粒率和破碎率之间的关系。首次引入破碎系数（CB）作为葡萄振动脱粒适宜性的指标。CB低于0.1的葡萄被认为不适合机械脱粒。最后，通过正交试验确定不同成熟度品种的最佳脱粒参数组合。研究结果可为酿酒葡萄机械化采收的优化提供参考。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computers and Electronics in Agriculture 工程技术-计算机：跨学科应用

CiteScore

15.30

自引率

14.50%

发文量

800

审稿时长

62 days

期刊介绍： Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.