基于电流变材料的车削切断颤振抑制研究

应用电流变材料的特殊性能——对电信号的快速响应能力和连续可变的阻尼，研制了一种智能切削颤振抑制结构(刀座刚度可变部分)，并将其附加在车床刀架上，建立了机床车副颤振实时监控系统，实现了机床车削切断过程的颤振抑制。实验结果表明，利用电流变材料智能切削颤振抑制结构可以对机床车削振动进行有效控制，刀具的振动幅值减小50％以上，且工件表面的加工质量有较大提高。     

智能车床的颤振实时辨识与在线抑制系统研究

颤振是切削加工中的一种不稳定现象,它会对生产效率、加工表面和机床零部件造成恶劣的影响。因此,在加工过程中避免颤振的发生具有非常重要的意义。提出一种颤振的实时辨识与在线抑制系统,在不额外加装驱动部件的情况下,实现数控机床上加工颤振的智能抑制。该加工颤振智能抑振系统包括三部分:第一,以加权小波包熵作为特征实时地监测车削加工过程,并在颤振开始孕育时就将其辨识出来;第二,一旦检测到颤振,采用基于尺度因子的插值傅里叶算法,将颤振频率实时估计出来;第三,根据颤振频率,计算出主轴转速扰动的幅值和频率,使主轴转速按照指定的幅值和频率周期性变化,及时地将颤振抑制在其孕育阶段。通过薄壁圆盘端面车削试验对该系统进行验证,结果表明,所提颤振实时辨识与在线抑制系统能在颤振的孕育阶段,成功地将颤振检测出来,并及时进行抑制,实现了智能化的颤振抑制。     

融合颤振控制的恒功率约束自适应加工方法研究

针对切削颤振降低恒功率约束自适应加工过程效率的问题,研究了融合颤振控制的恒功率约束自适应加工方法。通过切削试验分析了颤振对机床主轴功率的影响,给出了控制恒功率约束自适应加工过程中颤振的必要性。基于模糊理论开发了模糊控制器,通过调整主轴进给实现了切削过程的恒功率约束。基于变转速抑制切削颤振理论调整机床主轴转速,实现了颤振抑制。以加工效率为目标制定了机床主轴进给和转速的调整原则,实现了融合颤振控制的恒功率约束自适应加工。通过切削实验验证了研究结果的有效性。     

车削加工颤振稳定性可靠度蒙特卡罗法仿真

在工程实际中,车削系统刚度、阻尼及切削力等参数的随机性严重影响车削加工的稳定性。针对此问题,提出了一种车削加工再生型颤振稳定性可靠度计算方法。考虑随机因素的影响,采用蒙特卡罗数值模拟方法,研究车削加工再生型颤振稳定性的统计分布规律。建立车削加工再生型颤振动力学模型,采用拉氏变换获取机床车削的极限切削宽度及所对应的主轴转速。根据数控车床切削系统动力学参数的分布信息抽取样本,代入再生型颤振模型进行计算,获取极限切削宽度的样本,并统计其概率特性,以实际切宽是否小于极限切宽为判别条件提出一种基于蒙特卡罗模拟的车削加工再生型颤振稳定性可靠度预测方法。     

基于自抗扰控制的变速非圆车削技术研究

为了抑制非圆截面工件加工时的颤振,将变速加工应用于非圆截面车削中,设计了变速车削系统结构,分析了变速车削抑制颤振、提高稳定性的机理,设计了适于变速非圆车削的直线伺服单元,单元控制采用自抗扰控制技术.车床控制系统采用PMAC(Programmable multi-axis controller)时基控制法,实现刀具驱动进给和工件变速旋转的协调控制,完成非圆截面的变速车削.结果表明,直线伺服单元能很好地跟踪刀具目标值,非圆截面车削加工精度和稳定性得到了提高.     

松浦机械制作所新一代——复合加工机床

松浦机械制作所成功开发出了新型复合加工机床“CUBLEX-25”，它在以立式机床（MC）为原型、具备车床功能的复合加工机床上新增了研磨功能。如此一来，除现有复合加工机床的Mc部分具备的切削和车床部分具备的车削之外，一台机床可完成研磨工序。新型复合加工机床的基本结构和5轴控制型MC相同，     

基于自适应变分模态分解与功率谱熵差的机器人铣削加工颤振类型辨识

机器人铣削加工存在模态耦合颤振和再生颤振现象,有效地进行机器人铣削加工颤振类型的辨识是进行颤振精准抑制和保证加工质量的基础。为此,提出一种基于自适应变分模态分解与功率谱熵差的颤振类型辨识(AVMD-ΔPSE)方法。通过分析机器人铣削加工颤振特性和主导模态,将机器人铣削颤振分为机器人结构模态主导的模态耦合颤振和刀具-主轴结构模态主导的再生颤振两种类型。为了提取颤振敏感子信号,利用自适应变分模态分解方法对原始信号进行分解,根据功率谱熵和频率消除算法设计功率谱熵差颤振类型辨识指标,结合多组试验数据采用高斯混合模型自适应地确定辨识指标最佳分类阈值。颤振辨识试验表明机床铣削加工颤振辨识方法运用于机器人铣削加工中仅能识别颤振却无法区分不同的颤振类型,而AVMD-ΔPSE方法能准确有效地辨识和区分机器人铣削加工中的模态耦合颤振和再生颤振,为机器人铣削颤振的针对性抑制提供理论指导。     

基于四阶矩法车削颤振可靠性研究*

再生颤振是影响加工质量、加速刀具磨损、刀具破坏的主要原因。以车削加工为研究对象,针对具有不确定参数的车削加工颤振预测问题,研究车削加工系统结构动态特性参数具有随机特性的情况下颤振可靠性建模及求解问题。定义车削加工过程不出现颤振的概率为颤振可靠度,建立车削加工系统可靠性模型,研究四阶矩法求解可靠度的问题,提出利用颤振可靠性叶瓣图方法进行颤振预测。通过模态试验对一车床进行频响函数测试,采用四阶矩法计算获得了颤振可靠度,并与蒙特卡洛法获得的可靠度相比较。结果表明四阶矩法计算获得的可靠度与蒙特卡洛仿真结果一致性很好,但是四阶矩法计算精度高而且计算耗时远小于蒙特卡洛法。进行颤振可靠性切削试验,通过观察振纹和分析噪声功率谱识别颤振,对典型参数进行验证,试验结果与分析结果一致。     

铣削颤振在线智能控制方法研究

针对铣削加工中颤振降低加工效率的情况,研究了颤振在线自主识别与控制的方法。使用传感器拾取加工状态信息,提取振动信号时域均方差特征及频域幅度谱特征,提出了具有广泛适用性的颤振在线识别算法。以2π作为刀具前后两次铣削振纹间的最优相位差,依据颤振频率调整主轴转速,消除颤振。以SIEMENS 840D系统为平台研究了机床转速在线实时调整的方法。进行了铝合金工件的铣削实验,验证了颤振在线抑制、提高加工效率的有效性。     

加工误差补偿的内车削再生颤振辨识与鲁棒控制

为解决内部车削再生颤振以及加工误差问题,提出了一种基于加工误差补偿的内车削再生颤振辨识与鲁棒控制方法。首先通过第一分量法实现了切削力模型参数辨识。然后利用上一步确定的切削力模型参数,综合考虑与颤振再生特性相关的时滞项,以及切削力和柔性镗杆模型的不确定性,设计了一个鲁棒稳定控制器用于解决内部车削过程中再生颤振控制的关键问题。进一步前馈项来消除杆端偏转,有效实现了加工误差补偿。最后通过模拟内车削过程的实验证明提出方法可以得到切削系数和重叠因子的合理估计,并且可以提升切削过程颤振抗扰度,降低了切削力直流分量引起的柔性杆挠度误差。     

埋设管道智能施工机械手工作原理与运动学分析

研究了埋设管道智能施工机械手的机械结构,对管道施工机械手的机械原理进行了创新研究,通过机械手的回转连接机构、平移机构和自锁式叉式提升机构实现了机械手的空间运动,并校核了机械手的卡紧力;把机械手简化为5自由度的空间杆机构,利用D—H法建立机械手的运动方程,得到运动学的正解和逆解,采用矢量积法求解机械手末端操作器的速度,为机械手控制系统设计提供参考。     

车削过程异常智能化声发射(AE)监视系统的研究

本文阐述了一种车削过程异常智能化监视系统的。作原理。根据这一原理建立的专家系统型车削过程异常声发射监视装置，有效地提高了识别成功率，降低了误报率。     

基于CCD传感器的智能车辆控制系统设计

针对智能车辆控制系统的高速度、高精度的特点,结合CCD传感器获取的路径信息,设计了一种智能控制方案。采用变速积分PID算法快速跟踪并控制智能车辆的速度,实现柔性加减速;采用参数自整定模糊控制算法精确的控制舵机的转向,实现柔性转弯。实验结果表明:该控制系统集高鲁棒性、灵活性和可扩展性于一体,提升了自主运行的快速性和稳定性,并有效地解决了图像采集技术的误差和干扰。     

Investigated iterative convergences of neural network for prediction turning tool wear

This study investigates the iterative convergences of neural network for prediction turning tool wear. For the smart manufacturing, the intelligent predict     

基于智能化刃磨的车刀位姿调整

本文建立了外圆车刀各刀面的方程式，给出了各刀面从安装初始位姿到刃磨位姿的坐标变换过程．给出了车刀位姿调整机构的传动原理，为智能车刀刃磨机的研制提供了必要的技术保证。     

The relationship between acoustic emission signals and cutting phenomena in turning process

The development of intelligent manufacturing by using machine tools is advancing in leaps and bounds. To maintain accuracy in machining and in the interests of fail-safe operation, monitoring of the cutting state or the final machining is very important. Acoustic emissions (AE) comprise elastic stress waves produced as a result of the deformation and fracture of materials. By measuring the AE generated during a turning process, it is possible to estimate the state of the machining operation. The correlation between cutting phenomena and AE in a turning process was examined experimentally by using a steel workpiece and a cermet tool in a numerically controlled turning process. The process of formation of chips, the types of chip, and the shear angle all markedly affected the AE signals. There was a strong negative correlation between the shear angle and the AE signal level. Similar results were obtained for various feed rates and for workpieces of various degrees of hardness. Correlations related to surface roughness and to tool wear are also described that permit the evaluation of the state of the turning process.     

Self-organizing fuzzy control of constant cutting force in turning

Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have nonlinear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. Moreover, the database and fuzzy rules of a TFC are fixed after the design step and then cannot appropriately regulate ones real time according to the system output response and the desired control performance. To solve the above problem, this work develops a self-organizing fuzzy controller (SOFC) for constant cutting force control to evaluate control performance of the turning system. The SOFC continually updates the learning strategy in the form of fuzzy rules, during the turning process. The fuzzy rule table of this SOFC can be begun with zero initial fuzzy rules which not only overcome the difficulty in the TFC design, but also establish a suitable fuzzy rules table, and support practically convenient fuzzy controller applications in turning systems control. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The SOFC has a better control performance in constant cutting force control than does the TFC, as verified in experimental results.     

Self-organizing fuzzy control of constant cutting force in turning

Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have nonlinear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. Moreover, the database and fuzzy rules of a TFC are fixed after the design step and then cannot appropriately regulate ones real time according to the system output response and the desired control performance. To solve the above problem, this work develops a self-organizing fuzzy controller (SOFC) for constant cutting force control to evaluate control performance of the turning system. The SOFC continually updates the learning strategy in the form of fuzzy rules, during the turning process. The fuzzy rule table of this SOFC can be begun with zero initial fuzzy rules which not only overcome the difficulty in the TFC design, but also establish a suitable fuzzy rules table, and support practically convenient fuzzy controller applications in turning systems control. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The SOFC has a better control performance in constant cutting force control than does the TFC, as verified in experimental results.     

基于单片机的多功能智能小车的设计

采用80C52单片机为控制核心,利用光电传感器和红外反射式传感器作为探测系统,并且利用PWM技术控制电动小汽车行驶速度的快慢、转弯以及自动停车,设计了一个多功能智能小车。该智能小车可以实现自动寻线行走、自动避障,报警以及遥控等功能。整个系统的电路结构简单,可靠性能高。