基于粒子群优化LS-SVM的车刀磨损量识别技术研究

刀具的磨损状态直接影响产品加工质量、成本和效率,对刀具磨损量的实时监测识别具有重要意义。针对刀具磨损状态先验样本少和常规神经网络识别模型收敛速度慢、易陷入局部极小值等问题,提出了基于最小二乘支持向量机（LS-SVM）的刀具磨损识别方法,并针对支持向量机的惩罚因子和核参数对模型识别精度影响较大的问题,提出一种根据个体适应度来调整惯性权重的自适应粒子群算法进行自动参数寻优。以车削加工为研究对象,采集加工过程中的切削力信号,应用小波包分析技术提取反映刀具磨损状态的特征信息作为识别模型的输入,然后利用训练好的自适应粒子群算法优化后的LS-SVM识别模型进行刀具磨损量识别。实验结果表明,该自适应粒子群优化算法比标准粒子群优化算法参数寻优能力更强;粒子群优化LS-SVM模型能高效地实现刀具磨损量识别,与BP神经网络相比具有更高的精度,且所需样本数较少,训练速度更快。     

切削用量对球头铣刀加工铝合金零件表面粗糙度的影响

本文通过对球头刀铣削(包含平面和斜面的零件表面粗糙度)进行理论和试验研究。建立了两种零件粗糙度经验模型,并分析了铣削加工中表面粗糙度的影响因素。研究发现,刀具每齿进给量是影响零件表面粗糙度的主要因素,增大每齿进给量、切削深度和切削宽度对表面质量不利。在两种经验模型中,指数模型拟合效果好于多项式模型,为实际生产中预测表面粗糙度合理安排工艺方案提供了参考。     

刀具状态在线监测与智能识别方法研究

切削加工中刀具状态是影响加工质量的关键因素,刀具的磨损直接影响工件的加工精度和表面粗糙度.选择加速度传感器监测切削加工中的振动信号,针对刀具状态变化时振动能量分布随之变化的特点,提取不同频段振动能量作为特征量,利用RBF神经网络进行聚类辨识.实验结果表明,该方法具有良好的识别效果和工程应用价值.     

滚珠丝杠副在数控车床中的加工技术

作为机械制造业的基础性部件,滚珠丝杠大量应用于各行各业。在数控车床上加工滚珠丝杠因存在螺距大、螺纹升角大以及吃刀深等情况,切削过程中常发生断刀、振动以及表面粗糙度差等问题。其次,大螺距螺纹车削时对刀具的要求高,刀具准备不恰当会引起"啃刀"现象,导致表面粗糙度差。另外,由于螺距大、吃刀深,切削力随之增大,不     

基于特征提取和长短期记忆神经网络的铣刀磨损量预测

高速铣削加工过程中,铣刀的磨损会影响工件加工质量、生产效率和制造成本等,因而精准的刀具磨损量预测可优化生产决策,避免因刀具磨损而带来的损失.为了提高刀具磨损预测精度,文章提出了一种基于特征提取及长短期记忆神经网络(L S T M)的铣刀磨损量预测方法,该方法基于工业过程中产生的时间序列数据,通过对数据的时域特征、频域特...     

基于NX二次开发的数控刀具管理与选取系统

随着机械技术不断发展,在现代机械加工中,越来越多的企业引入加工中心、数控车床和数控镗铣床等数控加工设备,从而使数控刀具代替传统刀具,被大量应用在生产的第一线中,成为数控加工中的主要角色.在实际加工中,数控刀具的质量和性能对加工效率、工件尺寸精度和表面粗糙度等相关指标产生直接影响.因此刀具的性能、质量以及管理在对产品质量、生产成本和生产效率等企业效益方面起着极其重要的作用.据数据统计:16%的计划作业停止由于缺乏刀具造成,30%～60%的刀具库存不在控制之中,机床操作人员20%的时间花费在查找刀具上等.     

基于AutoCAD的数控加工可视化仿真系统研究

NC仿真是代替传统"试切"来验证NC加工程序正确性和合理性的重要措施.综合利用三维实体建模技术与数据库技术,在计算机仿真模型中,构建了包括毛坯、刀具、夹具及机床等部件的"真实"数控加工仿真环境.采用基于实体建模技术,布尔造型的方法,实现了数控加工过程刀具运动仿真、工件切削过程动态仿真以及碰撞干涉检验仿真.并建立了零件表面粗糙度的数学模型,预测三维零件的表面加工质量.     

基于ANFIS的刀具磨损预测

自适应神经模糊推理系统ANFIS是模糊控制与神经网络控制结合的产物.讨论了ANFIS的结构及其特点,以及对刀具数据的处理以及建模,实现刀具磨损在线预测功能.实验结果显示:利用ANFIS对铣床刀具磨损量进行监测,可以知道刀具加工多少次后进行更换维修,可应用于工厂中的机器设备,协助工厂管理者掌握工厂运作状况,及时作出最佳及...     

基于纹理分析的表面粗糙度等级识别

提出了一种利用图象纹理分析技术进行机械加工表面粗糙度检测的非接触检测方法,该方法首先根据统计方差对待测工件的表面粗糙度进行粗分类,然后,利用基于Ｇａｂｏｒ滤波器的纹理分类器,识别待测工件表面粗糙度等级。该新方法可简单、快速地实现表面粗糙度等级的自动识别,而且对图象旋转具有不变性,由于其纹理分类器的参数少,并且新方法成本低,参数标定方便,因而便于现场检测,如果与机床的控制系统相连,还可以实现加工的实     

表面粗糙度光学测量方法研究进展

表面粗糙度对工件的性能有很大的影响,由于机械、电子及光学工业的飞速发展,对精密机械加工表面的质量及结构小型化的要求日益提高,使得表面粗糙度测量显现出越来越重要的地位。采用光学方法测量表面粗糙度具有非接触、无损伤、测量精度高等优点。介绍了用光散射法、像散法、散斑法、光干涉法、光学触针法测量表面粗糙度的原理及研究进展,讨论了上述方法各自的优缺点,对表面粗糙度测量的发展方向进行了预测。     

Digital twin-driven surface roughness prediction and process parameter adaptive optimization

In the process of parts machining, the real-time state of equipment such as tool wear will change dynamically with the cutting process, and then affect the surface roughness of parts. The traditional process parameter optimization method is difficult to take into account the uncertain factors in the machining process, and cannot meet the requirements of real-time and predictability of process parameter optimization in intelligent manufacturing. To solve this problem, a digital twin-driven surface roughness prediction and process parameter adaptive optimization method is proposed. Firstly, a digital twin containing machining elements is constructed to monitor the machining process in real-time and serve as a data source for process parameter optimization; Then IPSO-GRNN (Improved Particle Swarm Optimization-Generalized Regression Neural Networks) prediction model is constructed to realize tool wear prediction and surface roughness prediction based on data; Finally, when the surface roughness predicted based on the real-time data fails to meet the processing requirements, the digital twin system will warn and perform adaptive optimization of cutting parameters based on the currently predicted tool wear. Through the development of a process-optimized digital twin system and a large number of cutting tests, the effectiveness and advancement of the method proposed in this paper are verified. The organic combination of real-time monitoring, accurate prediction, and optimization decision-making in the machining process is realized which solves the problem of inconsistency between quality and efficiency of the machining process.     

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

Nowadays, face milling is one of the most widely used machining processes for the generation of flat surfaces. Following international standards, the quality of a machined surface is measured in terms of surface roughness, Ra, a parameter that will decrease with increased tool wear. So, cutting inserts of the milling tool have to be changed before a given surface quality threshold is exceeded. The use of artificial intelligence methods is suggested in this paper for real-time prediction of surface roughness deviations, depending on the main drive power, and taking tool wear, \(V_{B}\) into account. This method ensures comprehensive use of the potential of modern CNC machines that are able to monitor the main drive power, N, in real-time. It can likewise estimate the three parameters -maximum tool wear, machining time, and cutting power- that are required to generate a given surface roughness, thereby making the most efficient use of the cutting tool. A series of artificial intelligence methods are tested: random forest (RF), standard Multilayer perceptrons (MLP), Regression Trees, and radial-based functions. Random forest was shown to have the highest model accuracy, followed by regression trees, displaying higher accuracy than the standard MLP and the radial-basis function. Moreover, RF techniques are easily tuned and generate visual information for direct use by the process engineer, such as the linear relationships between process parameters and roughness, and thresholds for avoiding rapid tool wear. All of this information can be directly extracted from the tree structure or by drawing 3D charts plotting two process inputs and the predicted roughness depending on workshop requirements.     

基于图像连通区域数的刀具磨损状态特征提取实验研究

为了利用计算机视觉技术进行刀具状态监测,设计了机械加工刀具状态监测实验系统,并通过将图像处理技术引入到机械加工刀具磨损状态监测中,提出了一种通过提取工件表面图像的连通区域数来判断刀具磨损状态的新方法。该方法首先采集被加工工件的表面图像;然后对图像进行预处理,并对区域行程算法进行了改进,再用改进的区域行程标记算法对机械加工工件表面图像进行标记;最后通过统计连通区域数来判断刀具的磨损状态。理论和实验分析表明,由于加工工件表面图像的连通区域数和刀具磨损有很强的相关性,其可以间接判断刀具磨损情况,从而可达到对刀具状态进行监测的目的。实验表明,该方法计算简单、识别速度快,可以有效地判断刀具的磨损状态。     

Effect of tool vibrations on surface roughness during lathe dry turning process

Choice of optimized cutting parameters is very important to control the required surface quality. In fact, the difference between the real and theoretical surface roughness can be attributed to the influence of physical and dynamic phenomena such as: built-up edge, friction of cut surface against tool point and vibrations. The focus of this study is the collection and analysis of surface roughness and tool vibration data generated by lathe dry turning of mild carbon steel samples at different levels of speed, feed, depth of cut, tool nose radius, tool length and work piece length. A full factorial experimental design (288 experiments ) that allows to consider the three-level interactions between the independant variables has been conducted. Vibration analysis has revealed that the dynamic force, related to the chip-thickness variation acting on the tool, is related to the amplitude of tool vibration at resonance and to the variation of the tool's natural frequency while cutting. The analogy of the effect of cutting parameters between tool dynamic forces and surface roughness is also investigated. The results show that second order interactions between cutting speed and tool nose radius, along with third-order interaction between feed rate, cutting speed and depth of cut are the factors with the greatest influence on surface roughness and tool dynamic forces in this type of operation and parameter levels studied. The analysis of variance revealed that the best surface roughness condition is achieved at a low feed rate (less than 0.35 mnt/rev), a large tool nose radius (1.59 mm) and a high cutting speed (265 m/min and above). The results also show that the depth of cut has not a significant effect on surface roughness, except when operating within the built-up edge range. It is shown that a correlation between surface roughness and tool dynamic force exist only when operating in the built-up edge range. In these cases, built-u edge formation deteriorates surface roughness and increases dynamic forces acting on the tool. The effect of built-up edge formation on surface roughness can be minimized by increasing depth of cut and increasing tool vibration. Key words:design of experiments, lathe dry turning operation, full factorial design, surface roughness, measurements, cutting parameters, tool vibrations.     

Tool wear modeling in rotary turning modified by ultrasonic vibration

Modification of conventional turning operation is carried out by using different methods to improve machinability conditions. In this study, rotary turning is modified by adding ultrasonic vibrations to cutting tool. Accordingly, the effect of this method on output parameters namely, tool wear and temperature, cutting force, and surface roughness, is investigated. Having detailed analysis, finite element method is used beside the experiments. As a result, it was revealed that tool-chip engagement time during rotary motion of cutting tool significantly reduced wear propagation on tool faces. This was explained by heat analysis in which disengagement time resulted in lower heat transfer from chip to tool. Moreover, the result of surface roughness produced in vibratory-rotary turning was compared by rotary one.     

Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling

In this study, micro-milling of AISI 304 stainless steel with ball nose end mill was conducted using Taguchi method. The influences of spindle speed, feed rate and depth of cut on tool wear, cutting forces and surface roughness were examined. Taguchi’s signal to noise ratio was utilized to optimize the output responses. The influence of control parameters on output responses was determined by analysis of variance. In this study, the models describing the relationship between the independent variables and the dependent variables were also established by using regression and fuzzy logic. Efficiency of both models was determined by analyzing correlation coefficients and by comparing with experimental values. The results showed that both regression and fuzzy logic modelling could be efficiently utilized for the prediction of tool wear, cutting forces and surface roughness in micro-milling of AISI 304 stainless steel.     

A geometrical model for surface roughness prediction when face milling Al 7075-T7351 with square insert tools

Surface quality is important in engineering and a vital aspect of it is surface roughness, since it plays an important role in wear resistance, ductility, tensile, and fatigue strength for machined parts. This paper reports on a research study on the development of a geometrical model for surface roughness prediction when face milling with square inserts. The model is based on a geometrical analysis of the recreation of the tool trail left on the machined surface. The model has been validated with experimental data obtained for high speed milling of aluminum alloy (Al 7075-T7351) when using a wide range of cutting speed, feed per tooth, axial depth of cut and different values of tool nose radius (0.8 mm and 2.5 mm), using the Taguchi method as the design of experiments. The experimental roughness was obtained by measuring the surface roughness of the milled surfaces with a non-contact profilometer. The developed model can be used for any combination of material workpiece and tool, when tool flank wear is not considered and is suitable for using any tool diameter with any number of teeth and tool nose radius. The results show that the developed model achieved an excellent performance with almost 98% accuracy in terms of predicting the surface roughness when compared to the experimental data.     

Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM,ANN and SVM

In this paper, statistical models were developed to investigate effect of cutting parameters on surface roughness and root mean square of work piece vibration in boring of stainless steel. A mixed level design of experiments was prepared with process variables of nose radius, cutting speed and feed rate. According to design of experiments, eighteen experiments were conducted on AISI 316 stainless steel with PVD coated carbide tools. Surface roughness, tool wear and vibration of work piece were measured in each experiment. A laser Doppler vibrometer was used to measure vibration of work piece in the form of acousto optic emission signals. These signals were processed and transformed in to different frequency zones using a fast Fourier transformer. Analysis of variance was used to identify significant cutting parameters on surface roughness and root mean square of work piece vibration. Predictive models like response surface methodology, artificial neural network and support vector machine were used to predict the surface roughness and root mean square of work piece vibration. Cutting parameters were optimized for minimum surface roughness and root mean square of work piece vibration using a multi response optimization technique.     

Condition monitoring of CNC machining using adaptive control

In this work, an adaptive control constraint system has been developed for computer numerical control (CNC) turning based on the feedback control and adaptive control/self-tuning control. In an adaptive controlled system, the signals from the online measurement have to be processed and fed back to the machine tool controller to adjust the cutting parameters so that the machining can be stopped once a certain threshold is crossed. The main focus of the present work is to develop a reliable adaptive control system, and the objective of the control system is to control the cutting parameters and maintain the displacement and tool flank wear under constraint valves for a particular workpiece and tool combination as per ISO standard. Using Matlab Simulink, the digital adaption of the cutting parameters for experiment has confirmed the efficiency of the adaptively controlled condition monitoring system, which is reflected in different machining processes at varying machining conditions. This work describes the state of the art of the adaptive control constraint (ACC) machining systems for turning. AISI4140 steel of 150 BHN hardness is used as the workpiece material, and carbide inserts are used as cutting tool material throughout the experiment. With the developed approach, it is possible to predict the tool condition pretty accurately, if the feed and surface roughness are measured at identical conditions. As part of the present research work, the relationship between displacement due to vibration, cutting force, flank wear, and surface roughness has been examined.