An intelligent sensor for detection of milling chatter

In this paper, a new real-time sensor system has been developed to detect chatter in milling operations. In the developed sensor system, a pattern recognition technique based on an unsupervised neural network using the adaptive resonance theory (ART) is adopted for detection of milling chatter. The features on the cutting force spectrum are fed into the sensor system to classify the milling process with or without chatter. The experimental results indicate that the proposed sensor system can accurately detect milling chatter regardless of the variation in cutting conditions.     

机械阻尼器对刀具颤振抑制的理论分析及数值模拟

为了抑制高速铣削中的颤振影响,文中提出在刀具中心插入一个分层梁结构机械阻尼器．依靠刀具与阻尼器间的相对运动产生的摩擦阻尼-以耗散振动能量。通过理论分析和数值模拟的方法分别计算了接触压力和阻尼耗散功率．并对两种结果比较分析．找出了造成结果差异的主要原因。     

Prediction of in-process frequency response function and chatter stability considering pose and feedrate in robotic milling

Chatter occurs easily during robotic milling owing to the low structural stiffness of industrial robots and can degrade the machining quality or even cause robot failure. The accurate frequency response function (FRF) of the robot is essential for predicting chatter stability and selecting the appropriate process parameters. However, the FRF of a robot is affected by multiple factors, such as pose, operating state, and external excitation. In this study, an in-process FRF prediction method considering robot pose and feedrate was developed and used to predict chatter stability. Firstly, the static FRFs were obtained from the experimental modal analysis for different robot poses and used to train a Gaussian process regression (GPR) model. Subsequently, the static FRF predicted using GPR and the modal parameters identified by operational modal analysis (OMA) were used to calculate the in-process FRFs of the robot in the operation state. After removing the harmonic components of the vibration signals using a matrix notch filter, OMA was conducted using the least-squares complex frequency. Furthermore, the FRF of the robot was transformed from the robot flange coordinate system into the engagement coordinate system using the kinematics model and the tool path. The dynamic milling model, considering tool and robot modes was used for predicting stability. Finally, the proposed method was demonstrated by time-domain simulation of the robot-tool system and milling tests, and the effects of the running state and feed direction on chatter stability considering robot mode were analyzed.     

高速铣削颤振识别和稳定性预测

颤振是刀具与工件之间剧烈的自激振动,是影响工件表面质量与刀具磨损的重要因素。通过高速铣削试验,对加工过程中铣削力与振动信号进行分析,给出了一种通过监测加工过程中信号功率谱能量比变化来识别颤振的方法。试验结果表明:颤振发生时信号功率谱最主要的特性是在主轴转动频率、切削频率及其谐波两边等间距处会出现相应的颤振频率,当主颤振频率处的能量超过一定的阈值时,加工系统颤振,否则,无颤振。建立了颤振动力学模型,通过试验获得了铣削系统频响函数和铣削力系数,绘制了铣削加工稳定性曲线。结合提出的颤振识别方法,验证了动力学模型的准确性,可为实际加工中合理选择加工参数和颤振监测提供参考。     

Investigation on chatter stability of robotic rotary ultrasonic milling

In recent years, industrial robots with higher flexibility and lower cost have become a hot topic in the manufacturing field. In terms of practical machining applications, they are mainly employed in the situations with low cutting forces such as deburring, chamfering and polishing. However, the weak stiffness of robot induces milling chatter easily. Severe chatter not only damages the dimensional accuracy of parts, but also decreases machining efficiency and tool life. Thus, it is urgent to seek a new method to suppress robotic milling chatter. In this paper, robotic rotary ultrasonic milling (RRUM) technology is used to restrict machining vibration. Meantime, an analytical model of stability is developed. Robotic milling system is considered as a three degrees of freedom (3-DOF) model. After that, based on analysis of dynamic chip thickness, a linear force model is developed through defining an angle γ affected by ultrasonic vibration. Then, the semi-discretization method (SDM) is applied to obtain stability lobe diagrams. The analysis result indicates that stability region of RRUM is improved by 133% compared with robotic conventional milling (RCM). Finally, verification experiments are carried out to prove the rationality and effectiveness of these stability lobe diagrams.     

Research on the directionality of end dynamic compliance dominated by milling robot body structure and milling vibration suppression

The end dynamic characteristics dominated by the milling robot's body structure play a crucial role in vibration control and chatter avoidance in robotic milling. As the excitation source, the milling force may exist in any direction under different process parameters. Consequently, investigating the directional distribution of the end dynamic characteristics becomes essential for studying the direction-dependent dynamic response of a milling robot. In this paper, firstly, the directionality of the end modal vibration is proved based on the body structure mode shape of the milling robot. Subsequently, combined with the multi-body dynamics model of milling robots, the distribution of the end dynamic compliance with the excitation direction in the robot mode is modeled and found to be double-sphere, which is verified experimentally. A convenient method for acquiring the double-sphere dynamic compliance (DSDC) is given and its portability is shown. Then, two application cases of the DSDC in milling vibration suppression are given. In Case 1, based on the DSDC, the milling vibration amplitude is found to be distributed as an eccentric ellipse with a non-orthogonal basis with the feed angle in a robot mode, wherein a feed direction selection method for reducing milling vibration without traversal calculation is given with experimental validation. Case 2 shows that according to the guidance of the DSDC, the tuned mass damper can significantly suppress the milling vibration. It is worth noting that the directionality of the end modal vibration and DSDC constitute fundamental dynamic properties of milling robots, which may provide a new theoretical basis for the research related to the robotic end dynamic characteristics (such as frequency response function identification, mode coupling chatter mechanism and its suppression, etc.), which are well worth exploring.     

Suppression of nonlinear regenerative chatter in milling process via robust optimal control

During the milling process, self-excited vibration or chatter adversely affects tool life, surface quality and productivity rate. In this paper, nonlinear cutting forces of milling process are considered as a function of chip thickness with a complete third order polynomial (instead of the common linear dependency). An optimal control strategy is developed for chatter suppression of the system described through nonlinear delay differential equations. Counterbalance forces exerted by actuators in x and y directions are the control inputs. For optimal control problem, an appropriate performance index is defined such that the regenerative chatter is suppressed while control efforts are minimized. Optimal control law is determined based on variation of extremals algorithm. Results show that under unstable machining conditions, regenerative chatter is suppressed effectively after applying the optimal control strategy. In addition, optimal controller guarantees robust performance of the process in the presence of model parametric uncertainties.     

Modeling and adaptive force control of milling by using artificial techniques

The contribution discusses the use of combining the methods of neural networks, fuzzy logic and PSO evolutionary strategy in modeling and adaptively controlling the process of ball-end milling. On the basis of the hybrid process modeling, off-line optimization and feed-forward neural control scheme (UNKS) the combined system for off-line optimization and adaptive adjustment of cutting parameters is built. This is an adaptive control system controlling the cutting force and maintaining constant roughness of the surface being milled by digital adaptation of cutting parameters. In this way it compensates all disturbances during the cutting process: tool wear, non-homogeneity of the workpiece material, vibrations, chatter, etc. The basic control principle is based on the control scheme (UNKS) consisting of two neural identifiers of the process dynamics and primary regulator. An overall procedure of hybrid modeling of cutting process used for creating the CNC milling simulator has been prepared. The experimental results show that not only does the milling system with the design controller have high robustness, and global stability, but also the machining efficiency of the milling system with the adaptive controller is 27% higher than for traditional CNC milling system.     

Modelling the dynamics of industrial robots for milling operations

Using industrial robots as machine tools is targeted by many industries for their lower cost and larger workspace. Nevertheless, performance of industrial robots is limited due to their mechanical structure involving rotational joints with a lower stiffness. As a consequence, vibration instabilities, known as chatter, are more likely to appear in industrial robots than in conventional machine tools. Commonly, chatter is avoided by using stability lobe diagrams to determine the stable combinations of axial depth of cut and spindle speed. Although the computation of stability lobes in conventional machine tools is a well-studied subject, developing them in robotic milling is challenging because of the lack of accurate multi-body dynamics models involving joint compliance able of predicting the posture-dependent dynamics of the robot. In this paper, two multi-body dynamics models of articulated industrial robots suitable for machining applications are presented. The link and rotor inertias along with the joint stiffness and damping parameters of the developed models are identified using a combination of multiple-input multiple-output identification approach, computer-aided design model of the robot, and experimental modal analysis. The performance of the developed models in predicting posture-dependent dynamics of a KUKA KR90 R3100 robotic arm is studied experimentally.     

Rapid prediction of posture-dependent FRF of the tool tip in robotic milling

During the robotic milling process, vibration is one of the main factors that affect the machining accuracy and surface quality due to the low stiffness of the robot structure. The robotic milling stability is a function of the frequency response function (FRF) at the tool tip, which is posture-dependent within the workspace. This paper introduces an approach for rapidly predicting the tool tip FRF for industrial robotic milling at any posture. In this method, the models of the one degree-of-freedom (DOF) robot and two DOF robot are extended to a six DOF industrial robot to calculate the FRF at the holder tip based on the FRF acquisition tests at the arranged postures and a standardization process. Considering the coupling effects between the holder and the tool, the tool tip FRF at any posture of the milling robot is calculated using the receptance coupling substructure analysis (RCSA) method. Accordingly, the proposed method is applied to an industrial robot, and the feasibility of this method for predicting the posture-dependent FRF at high frequency in the workspace is validated though the impact tests. Moreover, the stability lobe diagram is calculated and the chatter tests are performed to validate its accuracy. At last, the robot structural modes are observed at the low-frequency dominant modes, whose frequencies are around 10 to 20 Hz.     

Position-oriented process monitoring in milling of thin-walled parts

Improving machining performance of thin-walled parts is of great significance in aviation industry, since most aviation parts are characterized by large size, complex shape, and thin-walled structure. Machining process monitoring is the essential premise to improve the machining performance. In order to improve the machining quality and efficiency, this paper presents a position-oriented process monitoring model based on multiple data during milling process, and corresponding solution is provided. Through obtaining the internal data set of the numerical control (NC) system during machining, it is possible to correlate the cutting position with monitoring signals including cutting force, acceleration, and spindle power. Then, process optimization is realized to improve the machining quality and efficiency based on the monitoring results. Machining tests are conducted on aircraft structural part as well as blade part, and the experimental results show this method provides a significant insight into the machining process of thin-walled part and contributes to the process optimization. By using feedrate optimization, time consumption for the rough milling process of one titanium alloy part reduced from 19.1 h to 14.4 h and the number of cutter consumption dropped from 5 to 3. And according to the result of position-oriented process monitoring, the machining strategies were optimized to reduce vibration and avoid chatter, thereby improving the machining quality.     

An effective neural network and fuzzy time series-based hybridized model to handle forecasting problems of two factors

Fuzzy time series forecasting method has been applied in several domains, such as stock market price, temperature, sales, crop production and academic enrollments. In this paper, we introduce a model to deal with forecasting problems of two factors. The proposed model is designed using fuzzy time series and artificial neural network. In a fuzzy time series forecasting model, the length of intervals in the universe of discourse always affects the results of forecasting. Therefore, an artificial neural network- based technique is employed for determining the intervals of the historical time series data sets by clustering them into different groups. The historical time series data sets are then fuzzified, and the high-order fuzzy logical relationships are established among fuzzified values based on fuzzy time series method. The paper also introduces some rules for interval weighing to defuzzify the fuzzified time series data sets. From experimental results, it is observed that the proposed model exhibits higher accuracy than those of existing two-factors fuzzy time series models.     

Research on prediction of traffic flow based on dynamic fuzzy neural networks

Combining the advantages of the neural network and fuzzy system, this paper makes a further research on the dynamic fuzzy neural networks (D-FNN) traffic flow prediction. Instead of being in consistence with growth of the input number, the fuzzy rule number of the D-FNN increases exponentially in the whole training network structure. In particular, this method can establish a required network structure automatically. This method is applied to the traffic flow time series to analyze and compare the predicting performance of the predicting model based on the neural network method and the adaptive neural fuzzy inference system by combining with the chaos theory. The simulation result shows that this method is quite effective and can improve the predicting accuracy.     

基于模糊神经网络的高炉残铁渣量预测的研究

提出了利用模糊神经网络对高炉残铁量和残渣量预测的算法,论述了预测系统设计思路及网络结构的确定方法,最后应用实例验证该算法的可行性。对神经网络输出值和实际值的均方误差及对比曲线分析的结果表明,该方法对解决工程实际中预测问题具有一定的指导意义。     

基于Web的铣削过程颤振稳定域远程仿真系统研究

文章在分析数控加工铣削过程颤振稳定域仿真技术,介绍MATLABWebServer应用程序体系结构与开发原理的基础上,研究开发了基于Web的数控加工铣削过程颤振稳定域远程仿真系统;该系统实现了MATLAB语言与HTML语言的结合应用,为数控加工过程切削参数的合理、有效地选择提供了一种网络化远程仿真工具和方法,整个系统的主要功能在实际数控加工中得到了相应的验证。     

Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic

Artificial neural networks and fuzzy logic approaches have recently been used to model some of the human activities in many areas of civil engineering applications. Especially from these systems in the model experimental studies, very good results have been obtained. In this research, the models for predicting compressive strength of mortars containing metakaolin at the age of 3, 7, 28, 60 and 90 days have been developed in artificial neural networks and fuzzy logic. For purpose of building these models, training and testing using the available experimental results for 179 specimens produced with 46 different mixture proportions were gathered from the technical literature. The data used in the multilayer feed-forward neural networks and Sugeno-type fuzzy inference models are arranged in a format of five input parameters that cover the age of specimen, metakaolin replacement ratio, water–binder ratio, superplasticizer and binder–sand ratio. According to these input parameters, in the multilayer feed-forward neural networks and Sugeno-type fuzzy inference models, the compressive strength of mortars containing metakaolin are predicted. The training and testing results in the multilayer feed-forward neural networks and Sugeno-type fuzzy inference models have shown that neural networks and fuzzy logic systems have strong potential for predicting compressive strength of mortars containing metakaolin.     

Active regenerative chatter suppression during boring manufacturing process

Machining performance such as that of the boring process is often limited by chatter vibration at the tool–workpiece interface. Among various sources of chatter, regenerative chatter in cutting systems is found to be the most detrimental. It limits cutting depth (as a result, productivity), adversely affects surface finish and causes premature tool failure. Though the machining system is a distributed system, all current active controllers have been designed based upon a simplified lumped single degree of freedom cutting process model. This is because it was found that in the majority of cutting processes, there exists only one dominating mode. However, such simplification does have some potential problems. First, since the system itself is a distributed system, theoretically it consists of infinite number of vibration modes. When the controller is designed to control the dominating mode(s) only, the energy designed to suppress the particular mode(s) may excite the rest of the structural modes, which unavoidably causes the so-called spillover problem. Second, the success of the control design of such simplified single degree of freedom system relies on the availability of accurate model parameters (such as the effective mass, stiffness and damping), which is unfortunately very hard to acquire. This is because the global properties are varying with the metal removal process and the movable components of machine tool. In this paper, an active controller designed from wave point of view is used to absorb chatter vibration energy in a broad frequency band to improve machining performance of a non-rotating boring bar. In contrast to most of the current active chatter control design, the wave controller is designed based on the real distributed cutting system model. The main advantage of such a control scheme to chatter suppression is its robustness to model uncertainties. The control scheme also eliminates the control spillover problem.     

基于模糊PID控制的列车主动悬架振动控制研究

为了研究高速列车主动悬架系统横向振动控制,将研究对象简化为二自由度1/4车悬挂模型,研究了高速列车车体的横移、侧滚为变量的列车悬挂控制系统,采用德国轨道谱作为列车的激励拟合。算法控制器选择方面,在PID控制和模糊控制的基础上,建立模糊PID控制,并建立了基于模糊PID控制的高速列车主动悬架横向振动控制系统,通过MATLAB仿真生成关系曲线,验证了模糊PID控制算法具有更好的控制效果。     

基于计算智能算法的铣刀状态监测

本文提出了基于智能融合技术进行铣刀磨损量监测和预测方法。利用多传感器对切削力和振动信号进行监测,通过频率变换提取切削力特征量,采用小波包分解技术提取振动信号特征量。通过信号特征值的组合,分别探讨了几种计算智能数据融合技术-小波神经网络、遗传神经网络、遗传小波神经网络对刀具磨损量的预测效果。实验分析表明,本文提出的几种计算智能数据融合技术均能够有效地完成刀具磨损量预测。     

基于模糊神经网络的机械故障诊断方法的研究

本文针对机械传动系统典型零部件的故障，应用振动谱分析方法，给出故障诊断的模糊规则，并采用模糊神经网络实现诊断推理，文中举例说明了该诊断方法。