凸轮轴磨床工件旋转轴转速预测算法的研究

采用三次B样条曲线插补的凸轮磨削加减速能力不足,易产生过磨和少磨。根据凸轮磨削的数学模型,分析了砂轮进给轴运动的理论与实际速度、加速度、加加速度,提出了一种预测工件旋转轴转速的加工方法,在速度变化剧烈处自动降低工件旋转轴转速,以避免加速度和加加速度的变化对伺服系统造成的机械冲击,利用Matrix VB控件编程技术,设计了凸轮轴磨削软件,并将其移植到YTMK-CNC8326全数控高速凸轮轴磨床中。测试表明,采用该方法磨削的凸轮轴型线误差小于±0.01mm,工件表面粗糙度得到明显改善,实现了凸轮轴的精密加工。     

数控凸轮轴磨床跳档机构的设计

介绍数控凸轮轴磨床跳档机构的研制。通过机床数控系统控制伺服电机的运动,实现凸轮轴的凸轮轮廓生成曲线的连续磨削加工。     

中达PUTNC—H6数控系统在凸轮轴高速磨床上的应用

本文介绍了中达电通最新推出的PUTNC—H6系列通用数控系统在数控凸轮轴高速磨床上的应用。通过选择H6数控系统,该磨床在提高凸轮轴磨削质量,提高加工精度和效率以及安全防护等方面均得到很大的提升。     

基于曲率优化的机器人砂带磨削轨迹规划

针对复杂曲面零件砂带磨削编程效率低、精度差的问题,基于B样条曲线曲面重构和机器人离线编程技术,提出了一种根据关键接触点曲率值生成工业机器人磨削轨迹的方法.首先,利用零件表面上需要进行砂带磨削的关键接触点和积累弦长参数化法构造节点矢量,从而计算出磨削轨迹的B样条基函数;其次,根据控制顶点反求矩阵得到全部未知控制点和3次B样条加工曲线;然后,分析关键接触点之间的曲率变化率和弧长,对关键点细化生成符合磨削工艺要求的目标点;最后,通过求解双3次B样条插值曲面方程获得目标点的加工姿态.以水龙头磨削为例进行试验,结果表明曲率优化算法磨削的零件表面轮廓形状明显优于截面法,且其粗糙度值能稳定在0.082 μm左右,可以有效提高工件表面加工质量.     

平面凸轮轮廓在数控铣床的磨削加工

本文以某公司铜线凸轮轮廓的磨削加工为例,经修整磨削,砂轮半径值吻合该凸轮的过渡半径值,采用$TC_DP6 [T,D]刀具半径补偿指令在数控铣床磨削加工程序中自动赋值。实际磨削加工证明,该凸轮轮廓的磨削加工工序合理,基于CAM自动生成程序,加入参数编程指令、$TC_DP6[T,D]刀具半径补偿指令磨削加工的方法可行、简便,确保了凸轮轮廓加工的质量及效率。     

基于AE的砂轮智能修整技术研究

本文基于声发射(AE,Acoustic Emission)研究了高精度磨削过程中砂轮的在线智能修整技术。在分析砂轮磨损和修整机理的基础上探讨了声发射技术在砂轮磨损检测中的应用,着重研究了砂轮修整过程中砂轮修整进给量的控制、运动的控制、AE在线智能修整系统的流程和砂轮在线智能补偿的分析。     

CNC高速凸轮磨削的实现

数控凸轮磨床是由法那克3M 系统和改装的普通凸轮轴磨床组成,用标准数控语言,采用直线插补和园弧插补的方法加工,这种机床精度高,可靠性好,极大地提高了磨削加工水平。     

嵌入式磨削加工主动测量仪数据管理技术研究

针对嵌入式磨削加工主动测量控制系统的功能要求,提出了应用数据库对系统中大量数据进行管理的方案。对WinCE系统下常用嵌入式数据库的存取性能做了对比测试,并最终确定选用SQLite数据库进行数据管理。结合SQLite数据库在磨削加工主动测量控制系统中的应用,给出了磨削加工主动测量仪的数据库结构及其在Visual Studio 2008集成开发环境中的实现过程。研究结果表明,此方案的设计能够满足磨削加工主动测量仪对数据存取的实时性与一致性需求,对于磨削加工主动测量仪的研究和发展有着重要意义。     

基于ARM的嵌入式精密进给控制系统设计

在精密磨削加工中,为提高加工精度,满足精密加工中工作台快速响应的实时性要求,并结合磨削加工工艺的特点和需求,提出了精密进给控制器的设计方案,采用基于ARM的嵌入式微处理器代替8位单片机对压电陶瓷进行控制,实现工作台的精密进给.同时,系统还具有较好的网络功能和良好可扩展性,可应用于精密数控磨床.     

基于声发射技术的自学习磨削加工监控系统

介绍了声发射技术的基本原理及其在磨削加工中的应用,针对不同加工条件、不同工作参数下声发射信号所处的频段不同,提出采用自学习方法进行滤波频段选择,并设计了自学习的磨削加工监控系统.监控系统对声发射信号进行滤波,利用均方根电压(RMS)特征量监测磨削状态,反馈给CNC数控系统,从而减少空程时间,并防止砂轮与工件碰撞.试验结果表明:该系统能够有效地优化磨削过程.     

Combining rough set and case based reasoning for process conditions selection in camshaft grinding

Case Based Reasoning (CBR) is a novel paradigm that uses previous cases to solve new, unseen and different problems. However, redundant features may not only dramatically increase the case memory, but also make the case retrieval more time-consuming. Furthermore, camshaft grinding process is controlled by a number of process parameters, and it is more complex comparing with the ordinary cylindrical grinding. The process conditions are achieved by skilled and professional workers. Therefore, this research combines Rough set (RS) and CBR for process conditions selection in camshaft grinding, and Genetic Algorithm (GA) is developed to discretize condition features. Through the approach an optimal subset of process conditions can be selected quickly and effectively from a large database with a lot of cases, and complexity of computation of the similarity testing is significantly reduced. Moreover, the validity of the proposed solution is verified by the application of practical experiments for the process conditions selection in camshaft grinding.     

Creep feed grinding optimization by an integrated GA-NN system

The present work is aimed to optimize creep feed grinding (CFG) process by an approach using integrated Genetic Algorithm-Neural Network (GA-NN) system. The aim of this creep feed grinding optimization is obtain the maximal metal removal rate (MRR) and the minimum of the surface roughness (R _a ). For optimization, metal removal rate is calculated with a mathematic formula and a Back Propagation (BP) artificial neural-network have been used to prediction of R _a . The parameters used in the optimization process were reduced to three grinding conditions which consist of wheel speed, workpiece speed and depth of cut. All of other parameters such as workpiece length, workpiece material, wheel diameter, wheel material and width of grinding were taken as constant. The BP neural network was trained using the scaled conjugate gradient algorithm (SCGA). The results of the neural network were compared with experimental values. It shows that the BP model can predict the surface roughness satisfactorily. For optimization of creep feed grinding process, an M-file program has been written in ‘Matlab’ software to integrate GA and NN. After generation of each population by GA, firstly, the BP network predicts R _a and then MRR has been calculated with mathematic formula. In this study, the importance of R _a and MRR is equal in the optimization process. By using this integrated GA-NN system optimal parameters of creep feed grinding process have been achieved. The obtained results show that, the integrated GA-NN system was successful in determining the optimal process parameters.     

磨矿粒度动态过程的一种快速Monte Carlo仿真方法

磨矿是降低矿物粒度的工业过程,产品粒度是磨矿过程的关键质量指标. 由于磨矿粒度难以在线检测且磨矿生产过程具有综合复杂特性,难以采用传统控制方法实现磨矿粒度的控制. 因此,建立磨矿粒度和关键工艺参数的动态模型对于磨矿运行控制和优化具有重要意义. 采用总量平衡原理获得磨矿粒度的微分方程模型多数情况下无法获得解析解. 而基于Monte Carlo （MC）方法的磨矿粒度模型能够精确模拟磨矿粒度分布的动态变化,但是其仿真效率低难以实用. 本文针对这一问题提出一种新的MC仿真方法: 在定总量方法的基础上引入新的颗粒移除机制,在移除过程中动态地分配各个粒级颗粒数目并保持破裂前后各个粒级颗粒所占总颗粒数的百分比不变,避免颗粒移除过程中由于粒级差异导致的抽样误差,且避免MC仿真速度随着仿真推进下降的问题. 仿真实验验证表明,本方法能够在保证一定精度前提下显著提高磨矿粒度MC仿真的计算速度. 最后,通过一个实例介绍了本文仿真模型在磨矿优化控制中的应用.     

基于I-PSO算法和Simulink的湿式离合器优化设计

为提高湿式离合器的轻便性和可靠性,提出了一种I-PSO算法与MATLAB/Simulink相结合的湿式离合器优化设计新方法。对湿式离合器进行动力学分析,并基于MATLAB/Simulink搭建湿式离合器动力传递的仿真模型。引入模拟退火算法中对粒子进行扰动的思想对改进的粒子群算法再度进行改进,并基于某测试函数验证了算法改进的效果,选择离合器的滑磨功与体积为优化目标。最终联合改进粒子群算法与MATLAB/Simulink中建立的湿式离合器仿真模型对某具体型号湿式离合器进行多目标优化设计。结果表明,改进后的粒子群算法在寻优的速率和精度上有一定效果;优化后的湿式离合器与原设计相比,总目标函数缩小约40.12%,滑磨功减小了约61.8%,优化效果明显。     

基于SVM 的机器人高精度磨削建模

为了改进机器人磨削过程中对磨削量的控制,提出了一种基于SVM 回归的磨削过程建模方法,通过 分析与磨削量相关的一组可测变量——机器人进给速率、接触力、工件表面曲率,利用机器学习的方法建立回归模 型,对磨削量进行预测．这种方法可以避免逐一分析复杂的动力学参数．实验结果表明,该方法可以取得良好的效 果,模型的预测精度达到90%以上,基本满足实际加工的要求．     

基于适应度函数的无人船遗传算法航径规划

遗传算法通过适应度函数选取最优的路径,采用了无人船转弯半径来改进适应度函数,实现无人船遗传算法航径规划。考虑到无人船机动性能对航迹平滑性的要求,在初始种群中利用贝塞尔曲线优化方法,将原有的折线路径优化成光滑的曲线路径;在适应度函数中添加曲率判断,以无人船最小转弯半径为约束条件,设置曲线路径的最大曲率,最后通过适应度函数筛选出符合约束条件的光滑路径。仿真结果表明,所提出的方法能获得符合无人船最小转弯半径约束的光滑路径,相比于平滑算法,该方法的曲率更小,收敛速度更快。     

Adaptive parameter optimization approach for robotic grinding of weld seam based on laser vision sensor

Automatic robot grinding technology has been widely applied in the modern manufacturing industry. A flexible abrasive belt wheel used to grind the weld can avoid burns on the base material and improve the processing efficiency. However, when the robot grinds a weld seam, the material removal depth does not coincide with the feed depth because of the soft contact and uneven weld height, affecting the weld seam surface uniformity. Given these problems, an adaptive parameter optimization approach for the robotic grinding of a weld seam was proposed based on a laser vision sensor and a material removal model. First, the depth of weld seam removal was obtained by a laser vision sensor based on triangulation in real-time. Then, a macroscopic material removal model considering flexible deformation was established to determine the relationship between the weld height and process parameters, and the model coefficient was experimentally fitted to ensure the accuracy and reliability of the model. In addition, the data of real-time interaction structure between the robot controller and grinding system were obtained and used to unsure that the rotational speed of the belt wheel increased in the convex part and decreased in the concave part, in order to obtain a uniform weld seam surface. Comparative experiments were performed to verify the effectiveness and superiority of the method, and experiments on the surface roughness and weld seam surface height difference were conducted to verify the universality of the method. Experimental results show that the residual height of the weld after grinding can be controlled within 0.2mm, and the maximum removal height difference can be controlled within 0.05mm. The surface roughness Ra of the weld after grinding could reach 0.408 µm.