林业科学 ›› 2026, Vol. 62 ›› Issue (7): 253-266.doi: 10.11707/j.1001-7488.LYKX20250378
• 综合评述 • 上一篇
袁湘月1,陈如瑜1,张子康1,任涛1,周建波2,陈忠加1,*(
)
收稿日期:2025-06-09
出版日期:2026-07-10
发布日期:2026-07-16
通讯作者:
陈忠加
E-mail:chenzhongjia@bjfu.edu.cn
基金资助:
Xiangyue Yuan1,Ruyu Chen1,Zikang Zhang1,Tao Ren1,Jianbo Zhou2,Zhongjia Chen1,*(
)
Received:2025-06-09
Online:2026-07-10
Published:2026-07-16
Contact:
Zhongjia Chen
E-mail:chenzhongjia@bjfu.edu.cn
摘要:
在国家政策有力引导和竹产业规模不断扩张的双重驱动下,竹制品加工需求迅猛增长。作为竹材初加工的核心设备,破竹机的技术水平直接影响产业发展效率和产品质量。为明晰其技术发展脉络,本研究运用文献分析法系统梳理相关理论和技术成果。在理论研究方面,学者们聚焦竹材物理力学特性和破竹力学过程,深入探究破竹速度、刀具角度等参数对破竹质量的影响机制,为设备优化设计提供了重要理论依据。在技术应用层面,破竹机经历从手动、半自动到全自动的演化过程,实现了从人工操作到智能化加工的升级。在关键技术方面,结构设计持续优化,通过开发可调式刀盘和自适应对中机构,有效提升了设备对不同规格竹材的适应性;检测技术取得突破,基于机器视觉的竹径识别系统使得直径测量更为精准可靠;控制系统实现智能化升级,集成PLC与多种传感器构建了闭环控制系统。这些技术进步显著提升了破竹机的加工精度、效率和适应性,为竹材加工自动化提供了可靠的技术支撑。然而,当前破竹机仍存在竹径识别精度不足、刀盘设计与加工效率矛盾突出、对复杂竹材适应性较弱以及智能化程度有限等技术瓶颈,难以满足大规模高精度生产需求。为进一步提升破竹机技术的性能和适应性,推动竹材加工产业高质量发展,提出未来破竹技术研究的重点方向:1) 针对竹材生物力学特性的多样性,深化破竹力和竹材结构参数的作用机制研究,建立多因素耦合的破竹理论模型,为设备优化设计提供科学依据;2) 开发基于深度学习的竹材智能识别系统,融合多光谱成像与三维点云技术,实现复杂工况下竹径、竹节等特征的精准检测和自适应定位;3) 创新破竹机构设计,突破传统旋转刀盘的结构限制,研发复合式切割装置和动态调节系统,解决破竹效率与质量之间的矛盾;4) 构建破竹机智能化生产体系,集成物联网监控、大数据分析和自主决策技术,实现设备状态实时监测、工艺参数动态优化和生产全流程智能管理。这些技术创新将推动破竹机向智能化、高效化、绿色化方向发展,为竹材加工产业高质量发展提供强有力的装备支撑,同时也将为我国竹材装备制造业的转型升级提供新的技术路径。
中图分类号:
袁湘月,陈如瑜,张子康,任涛,周建波,陈忠加. 破竹机技术发展与应用现状[J]. 林业科学, 2026, 62(7): 253-266.
Xiangyue Yuan,Ruyu Chen,Zikang Zhang,Tao Ren,Jianbo Zhou,Zhongjia Chen. Development and Application Status of Bamboo Splitting Machine Technology[J]. Scientia Silvae Sinicae, 2026, 62(7): 253-266.
图6
破竹机结构设计代表性研究(杨春梅等,2015;常飞虎等,2019;刘贯飞,2024;雷日扬,2017) a. 数控剖竹机上料机构Numerical control bamboo slicing machine feeding mechanism;b. 分料部分Feeding section;c. 竹段自动检测、上料机构设计Design drawing of automatic bamboo section detection and feeding mechanism;d. 浮动刀盘三维模型Three-dimensional model of floating cutter disc;e. 新型竹筒破削机的三维仿真结构设计Three-dimensional simulation structure design drawing of the new bamboo tube cutting machine."
表1
破竹机检测技术代表性研究性能参数对比"
| 技术类型 Technology type | 研究团队及年份 Research team & year | 核心方法/算法 Core method/algorithm | 关键性能参数(误差/精度) Key performance parameters (error/accuracy) |
| 机器视觉检测 Machine vision inspection | 多尺度细节增强+自适应双阈值 Multi-scale detail enhancement + adaptive dual threshold | 尺寸测量平均误差0.9% Average dimension measurement error 0.9% | |
| 机器视觉检测 Machine vision inspection | 中值滤波+二值化+Canny Median filtering + binarization + Canny | 尺寸识别准确率≥95%,误差±5% Dimension recognition accuracy ≥95%, error ±5% | |
| 机器视觉检测 Machine vision inspection | 梯度模板+改进 Canny+椭圆拟合 Gradient template + improved Canny + ellipse fitting | 尺寸误差≤2%,竹节定位准确率95.5% Dimension error ≤2%, bamboo node localization accuracy 95.5% | |
| 三维测量技术 3D measurement technology | YOLOv4-Tiny+MobileNet-SegNet | 外径误差1.43%,厚度误差8.76% Outer diameter error 1.43%, thickness error 8.76% | |
| 三维测量技术 3D measurement technology | 选择式掩模平滑+Sobel 算子 Selective mask smoothing + Sobel operator | 厚度误差±5%,内周长误差±2% Thickness error ±5%, inner circumference error ±2% |
表2
破竹机控制技术方案核心性能参数对比"
| 技术类型 Technology type | 研究团队及年份Research team & year | 核心技术手段 Core technical means | 关键性能参数(量化结果) Key performance parameters (quantified results) | 特点 Characteristics |
| 自动化控制系统 Automation control system | PLC+弹性机构测质量+自动换刀 PLC + elastic mechanism measurement + automatic tool change | 实现自动化换刀控制 Achieved automatic tool change control | 降低人工选刀依赖 Reduced reliance on manual tool selection | |
| 自动化控制系统 Automation control system | PLC闭环控制+电子尺测量 PLC closed-loop control + electronic ruler measurement | 破竹速度 16.7 m·min?1(较人工提升2倍),出材率63.1%,毛边率7.6% Splitting speed: 16.7 m·min?1 (2× improvement compared to manual), yield 63.1%, trimming rate 7.6% | 效率提升,闭环控制 Efficiency improvement, closed-loop control | |
| 自动化控制系统 Automation control system | ARM+RS485通信+多任务控制 ARM + RS485 communication + multi-task control | 实现选刀与对心逻辑控制 Achieved tool selection and centering logic control | 多任务协同控制 Multi-task collaborative control | |
| 智能控制算法 Intelligent control algorithm | 机器视觉+改进 Canny 算子 Machine vision + improved Canny operator | 出材率73%(优于传统圆/ 椭圆模型) Yield 73% (superior to traditional circular/elliptical models) | 动态优化剖分参数,出材率提升 Dynamic optimization of splitting parameters, improved yield | |
| 智能控制算法 Intelligent control algorithm | Python+椭圆分段拟合模型 Python + elliptical segmented fitting model | 出材率85% 以上,实时显示参数 Yield above 85%, real-time parameter display | 出材率提升 Improved yield |
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