三维集成车削力传感器数据采集与分析系统设计

切削力与切屑形成、切削热、刀具磨损和切削振动等现象有着密切联系,是影响加工精度、刀具寿命和切削效率的重要因素.通过实时测量切削力,及时调整切削参数、优化切削工艺,对于保证加工质量、延长刀具寿命、提高切削效率等有着重要意义.切削力的准确测量和处理离不开优良的数据采集与分析系统,针对基于MEMS压阻式芯片的三维集成车削力传感器,以微处理器STM32为控制核心研制了一种三维集成车削力传感器数据采集与分析系统,实现了三维车削力的标定、实时采集和数据分析功能.     

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。     

小波包分析在刀具声发射信号特征提取中的应用

分析了刀具的切削状态,介绍了刀具的声发射信号检测系统和小波、小波包分析技术,以及小波包频带能量分解方法,提出了小波包分解功率监测特征量提取技术.通过在刀具声发射的一个实例信号中的应用,有效地区分了刀具的两种切削状态,验证了小波包分解功率监测特征量提取方法的可行性.     

基于小波神经网络监测刀具状态的研究

针对切削过程中振动信号和AE信号的特点，提出一种基于小波分析和BP神经网络的刀具磨损监测系统。该系统能融合振动和AE信号的特征，描述信号特征与刀具状态的非线性关系，以此识别刀具状态。试验表明基于小波神经网络的刀具磨损状态监剩系统是有效的。     

一种曲线路径剪裁中刀具形变及其误差分析

动态移动切削阻力载荷对高速数控裁床加工过程中刀具形变及其剪裁误差具有的重要影响,提出了一种适用多层布料/皮革曲线剪裁路径的刀具形变及其误差计算方法;建立了动态负载条件下可伸缩刀具的挠度与转角方程,进而推导出高频振动裁刀剪裁误差及其随切削深度变化规律;计算结果表明,数控布料/皮革剪裁刀的动态载荷、高频振动参数、切削深度对剪裁误差具有重要影响,深入剖析高层数控裁床的加工机理,动态参数数据分析,对于提高机床加工效率,降低加工误差,提高刀具使用寿命具有一定的工程应用价值。     

车床系统切削稳定性研究

切削系统是一个复杂的动态系统,其系统动态特性对加工质量、生产效率、生产成本乃至整个加工系统的安全寿命起着至关重要的影响和制约作用。对于普通机床而言,其切削过程中的抗振性和稳定性是最受用户关注的。本文以CA6136车床为研究对象,通过切削振动试验对机床进行了切削稳定性分析。设计并实施了切削振动试验,识别系统的颤振频率,并通过对刀具系统的模态锤击试验,确定了刀具系统为颤振的主动体。分析了切削加工中最常出现的再生型颤振的机理,建立了再生型颤振的动力学模型,并根据实验数据得到了该车床的切削稳定性叶瓣图。     

车床系统切削稳定性研究

切削系统是一个复杂的动态系统,其系统动态特性对加工质量、生产效率、生产成本乃至整个加工系统的安全寿命起着至关重要的影响和制约作用。对于普通机床而言,其切削过程中的抗振性和稳定性是最受用户关注的。本文以CA6136车床为研究对象,通过切削振动试验对机床进行了切削稳定性分析。设计并实施了切削振动试验,识别系统的颤振频率,并通过对刀具系统的模态锤击试验,确定了刀具系统为颤振的主动体。分析了切削加工中最常出现的再生型颤振的机理,建立了再生型颤振的动力学模型,并根据实验数据得到了该车床的切削稳定性叶瓣图。     

中国微米纳米-三维集成车削力传感器数据采集与分析系统设计

切削力与切屑形成、切削热、刀具磨损和切削振动等现象有着密切联系,是影响加工精度、刀具寿命和切削效率的重要因素。通过实时测量切削力,及时调整切削参数、优化切削工艺,对于保证加工质量、延长刀具寿命、提高切削效率等有着重要意义。切削力的准确测量和处理离不开优良的数据采集与分析系统,本文针对基于MEMS压阻式芯片的三维集成车削力传感器,以微处理器STM32为控制核心研制了一种三维集成车削力传感器数据采集与分析系统,实现了三维车削力的标定、实时采集和数据分析功能。     

浅论车削加工中的振动与控制

在机械加工中产生的振动都具有受迫振动和自激振动,与机床、夹具、刀具和工件组成的工艺系统的动态特性有关。详细分析了车削加工中振动的主要类型及产生的原因、振动的危害,并从刀具、夹具、切削工艺等方面提出了减小或消除振动的措施。     

南京航空学院研制成功“计算机辅助切削试验数据采集与处理系统”

南京航空学院切削加工研究室研制的“计算机辅助切削试验数据采集与处理系统”于1987年5月16日通过了航空工业部组织的技术签定。该系统包括刀具磨损测量系统、切削力测量系统、切削温度测量系统、车床主轴转速自动检测控制系统以及刀具振动测量系统,功能齐全,软件丰富,可以在各种试验条件下进行刀具耐用度、切削力、切削温度及快速刀具     

Real-time tool breakage monitoring based on dimensionless indicators under time-varying cutting conditions

Tool breakage occurs randomly during machining operations, which induces more severe impacts on the quality of components compared to progressive tool wear. It is widely acknowledged that the unpredictable changes in cutting conditions will cause fluctuations in the signal amplitude and thus generate false alarms. This study introduced a novel method for tool breakage monitoring based on dimensionless indicators under time-varying cutting conditions. The amplitude ratio (AR) and the energy ratio (ER) were proposed according to the power spectrum of the spindle vibration signal, which represents the change of amplitude and the energy distribution, respectively. The AR and ER are normalized and integrated into a unified indicator for real-time breakage monitoring. The floating monitoring threshold is designed based on the Gaussian distribution. Moreover, the material removal rate (MRR) is selected as a secondary indicator to accurately identify tool breakage based on determining the amplitude fluctuation caused by cutting conditions or teeth breakage. The effectiveness of the proposed method for tool breakage monitoring has been verified under the constant, time-varying, and entry/exit cutting conditions. The results show that the proposed indicators have higher sensitivity than the traditional root mean square (RMS) features and eliminate false alarms during condition change transients. This research provides a potential solution for tool breakage monitoring under complex cutting conditions.     

A sensor fusion and support vector machine based approach for recognition of complex machining conditions

During the machining process of thin-walled parts, machine tool wear and work-piece deformation always co-exist, which make the recognition of machining conditions very difficult. Existing machining condition monitoring approaches usually consider only one single condition, i.e., either tool wear or work-piece deformation. In order to close this gap, a machining condition recognition approach based on multi-sensor fusion and support vector machine (SVM) is proposed. A dynamometer sensor and an acceleration sensor are used to collect cutting force signals and vibration signals respectively. Wavelet decomposition is utilized as a signal processing method for the extraction of signal characteristics including means and variances of a certain degree of the decomposed signals. SVM is used as a condition recognition method by using the means and variances of signals as well as cutting parameters as the input vector. Information fusion theory at the feature level is adopted to assist the machining condition recognition. Experiments are designed to demonstrate and validate the feasibility of the proposed approach. A condition recognition accuracy of about 90 % has been achieved during the experiments.     

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.     

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.     

In-process tool condition forecasting based on a deep learning method

It is widely acknowledged that machining precision and surface integrity are greatly affected by cutting tool conditions. In order to enable early cutting tool replacement and proactive actions, tool wear conditions should be estimated in advance and updated in real-time. In this work, an approach to in-process tool condition forecasting is proposed based on a deep learning method. A long short-term memory network is designed to forecast multiple flank wear values based on historical data. A residual convolutional neural network is built to enable in-process tool condition monitoring, using raw signals acquired during the machining process. The integration of them enables in-process tool condition forecasting. Median-based correction and mean-based correction are adopted to improve the accuracy. IEEE PHM 2010 challenge data has been used to illustrate and validate this approach. Experimental study and quantitative comparisons showed that future flank wear values could be precisely forecasted during the machining process. The proposed approach contributes to prompt and reliable cutting tool condition forecasting, which will support the decision-making about cutting tool replacement in data-driven smart manufacturing.     

Characterization of closed-loop measurement accuracy in precision CNC milling

This study investigates the closed-loop measurement error in computer numerical controlled (CNC) milling as they relate to the different inspection techniques. The on-line inspection of machining accuracy using a spindle probe has an inherent shortcoming because the same machine-produced parts are used for inspection. In order to use the spindle probe measurement as a means of correcting deviations in machining, the magnitude of measurement errors needs to be quantified. The empirical verification was made by conducting three sets of cutting experiments, followed by a design of experiment with three levels and three factors on a state-of-the-art CNC machining center. Three different material types and parameter settings were selected to simulate a diverse cutting condition. During the cutting, the cutting force and spindle vibration sensor signals were collected and a tool wear was recorded using a computer vision system. The bore tolerance was gauged by a spindle probe as well as a coordinate-measuring machine. The difference between the two measurements was defined as a closed-loop measurement error and the subsequent analysis was performed to determine the significant factors affecting the errors. The analysis results showed the potential of improving production efficiency and improved part quality.     

Design of an optimal damper to minimize the vibration of machine tool structures subject to random excitation

The vibration of machine tools during machining adversely affects machining accuracy and tool life, and therefore must be minimized. The cutting forces for stable turning are generally known to be random, and hence excite all the resonance modes. Of all these modes, those that generate relative motions between a cutting tool and a workpiece are of concern.This paper presents a new approach for designing an optimal damper to minimize the relative vibration between the cutting tool and workpiece during stable machining. An approximate normal mode method is employed to calculate the response of a machine tool system with nonproportional damping subject to random excitation. The major advantage of this method is that it reduces the amount of computation greatly for higher-order systems when responses have to be calculated repeatedly in the process of optimization. An optimal design procedure is presented based on a representative lumped parameter model that can be constructed by using existing experimental or analytical techniques. The two-step optimization procedure based on the modified pattern search and univariate search effectively leads the numerical solution to the global minimun irrespectively of initial values even under the existence of many local minima.