A study on the machining characteristics in the external plunge grinding using the current signal of the spindle motor

This paper describes the machining characteristics of external plunge grinding. The study investigates the process using the current signals of a spindle motor through a hall sensor. Grinding experiments were conducted under various grinding conditions such as wheel speeds, workpiece speeds and infeed rates with a conventional vitrified bonded wheel. Analyzing the current signal of the spindle motor, a relationship between current signals and the metal removal rate in terms of the infeed rate is induced. It was also shown that a hall sensor has similar capabilities in evaluation of grinding behavior compared to the AE signals, which are useful for monitoring the grinding process.     

Dynamic Compensation of Spindle-Integrated Force Sensors

This paper presents a dynamically compensated Spindle-Integrated Force Sensor system to measure cutting forces. Piezo-electric force sensors are integrated into the stationary spindle housing to measure cutting forces in three directions. The transfer function of the spindle structure between the cutting forces acting on the tool tip and the measured forces at the spindle housing are identified. Using the cutting force signals measured at the spindle housing, a Kalman filter is designed to filter the influence of structural modes on the force measurements. The frequency bandwidth of the force measurement system is significantly increased with the proposed sensor and the signal processing method. Milling experiments with tooth passing frequencies up to 1000 Hz are presented with effective removal of cutting force distortions caused by three structural modes of the spindle.     

Multi-Sensor Monitoring System in Chemical Mechanical Planarization (CMP) for Correlations with Process Issues

In this paper, three different sensors were used to measure multi-scale phenomena in chemical mechanical planarization. A piezoelectric force sensor, Hall effect sensor and acoustic emission sensor (AE) were installed in CMP equipment and the signals were measured simultaneously during the polishing process. The results showed that the sensors measuring frictional behaviour, such as the Hall effect sensor and force transducer, produced a clear end point signal in the case of the friction characteristics are distinguishable for each material. Also, if there is difference in hardness between materials, then a sharp end point signal is detected with the AE sensor even though the friction characteristic is similar between the two materials. Therefore, using multi-sensors having different bandwidths is complementary for not only process monitoring but also end point detection.     

High frequency bandwidth cutting force measurement in milling using capacitance displacement sensors

This article presents a method of measuring cutting forces from the displacements of rotating spindle shafts. A capacitance displacement sensor is integrated into the spindle and measures static and dynamic variations of the gap between the sensor head and the rotating spindle shaft under cutting load. To calibrate the sensing system, the tool is loaded statically while the deflection of the tool is measured with the capacitance probe. With this calibration, the displacement sensor can be used as an indirect force sensor. However, the measurement bandwidth is limited by the natural modes of the spindle structure. If cutting force frequency contents are within the range of the natural modes of the spindle structure or higher, the measurements are distorted due to the dynamic characteristics of the spindle system. In order to increase the bandwidth of the indirect force sensor by compensating for the spindle dynamics, the design of a Kalman filter scheme, which is based on the frequency response function (FRF) of the displacement sensor system to the cutting force, is presented in this paper. With the suggested sensing and signal processing method, the frequency bandwidth of the sensor system is increased significantly, from 350 to approximately 1000 Hz. The proposed indirect force sensor system is tested experimentally by conducting cutting tests up to 12,000 rpm with a five-fluted end mill. Besides cutting forces, the measured displacements can also be affected by factors such as roundness errors, unbalance at different speeds, or dilatation of the spindle shaft due to temperature variations. Methods to compensate for these disturbing effects are also described in the paper.     

Development and Application of a Wheel Based Process Monitoring System in Grinding

As an advantage to conventional monitoring systems sensor equipped grinding wheels offer the possibility to gain information on the process status from direct measurements of physical quantities in the contact zone. This can be realized by the integration of small temperature and force sensors into segmented grinding wheels. A new thermocouple sensor concept was developed whose novelty is the continuous contacting of the thermocouple by the grinding wheel wear. Further tests where conducted using a piezoelectric sensor integrated into the grinding wheel. By this set-up, forces in grinding as well as in dressing processes were obtained. After assessing the system's capability for monitoring grinding and dressing processes tests in an industrial environment showed the reliability of the monitoring system which therefore may become the basis for a novel kind of process control in the future.     

Design and analysis of a prototypical sensory Z-slide for machine tools

Due to the advancements in high speed and high performance cutting, further improvements of machine component design and process monitoring are necessary. For this purpose, new machine components with process monitoring capabilities have to be developed. In this paper, a new spindle carrying Z-slide for a 5-axis machining center with integrated sensing capabilities for process monitoring is presented. First, the overall system design is described. The sensing capabilities to enable process monitoring are realized by application of a micro-strain gauges network on to the structure of the slide. The optimal sensor positions are computed by application of a special sensor placement algorithm. The prototype of the slide has been built up to investigate the system behavior. The electronic system of the prototype to realize the signal amplification and the communication via an industrial bus are presented. Furthermore, the results of the system analysis of the prototype are described. Because the sensor amplitudes, which can be monitored by strain gauges on stiff structures, are generally small, a method to increase these amplitudes by use of the notch effect and new micro-strain sensors is discussed. With this method, the signal amplitudes can be increased significantly, without degrading the stiffness noticeably. At the end of the paper a method to manufacture notches in the prototype by a milling process is presented. The surface roughness of plan notch ground, measured with a laserprofilometer, shows roughness values lower than 3 μm.     

Sensorless tool failure monitoring system for drilling machines

It is well known that on-line tool condition monitoring has great significance in modern manufacturing processes. In order to prevent possible damages to the workpiece or the machine tool, reliable techniques are required providing an on-line response to an unexpected tool failure. Drilling is one of the most fundamental machining operations and two of the most crucial issues related to it are tool wear and fracture. During the spindle process, the motor driver current is related to the drill condition: power consumption is higher for a worn drill in comparison to a sharp drill for the same process. This difference in power consumption can be self-correlated to obtain the resulting waveform variance to provide a merit figure for tool condition. This paper describes a driver current signal analysis to estimate the tool condition by using the discrete Wavelet Transform in order to extract the information from the original cutting force, and through an autocorrelation algorithm evaluate the tool wear in the form of an asymmetry weighting function. The current is monitored from the motor driver to give a sensorless approach. Experimental results are presented to show the algorithm performance, a complete sensorless tool failure system which allows the detection of tool failure as a function of spindle current in real time.     

Evaluation of a spindle-based force sensor for monitoring and fault diagnosis of machining operations

This paper presents an evaluation of a spindle-based force sensor, the force ring, which is a strong candidate for in-process monitoring and fault diagnosis of machining operations. The evaluation criteria include the effect of integration of the force ring on the dynamics of the spindle, the drift behavior due to temperature change inside the spindle, the cross-talk between the channels, and the existence and compensation of the spindle-internal forces. The application of the force ring to process model-based monitoring and fault diagnosis has been demonstrated by the determination of force calibration coefficients using the force ring and their comparison with results obtained with a platform dynamometer. It has been concluded that the force ring provides dynamometer quality force measurements.     

基于改进瞬时刚性力模型的机床铣削加工监测

针对用于切削力预测的瞬时刚性力模型所需参数较多且依赖初步切削实验的问题,提出一种不需要切削实验的新型切削力预测方法,实现在实际工厂中监测机床铣削加工过程。在斜角切削模型和正交切削理论的基础上,对传统的瞬时刚性力模型进行改进,减少切削力预测所需的切削参数。改进后的模型仅需在铣削操作开始时从测量的主轴电机扭矩得到的剪切角参数,无需任何额外的传感器就可以实现铣削力预测。在所提模型中,刀具跳动的影响可通过每个切削刃处的旋转半径偏差表示,以精确预测切削力。为验证该模型的有效性,进行切削实验。结果表明：切削力的预测值与实测值吻合较好,在实际加工过程中,无需任何实验铣削或任何额外的力传感器就可以准确了解机床加工状态。     

Power signal separation in milling process based on wavelet transform and independent component analysis

Among many machining condition monitoring systems, a spindle motor power monitoring system is considered as one of the most popular systems for plant floor applications. However, in practice, power signals are mixed with many signal sources relevant to cutting tool, cutting conditions as well as components of a machine tool, which contaminate with each other in feature extraction processes and decrease the monitoring reliability. In this paper, modified blind sources separation (BSS) technique is used to separate those source signals in milling process. A single-channel BSS method based on wavelet transform and independent component analysis (ICA) is developed, and source signals related to a milling cutter and spindle are separated from a single-channel power signal. The experiments with different tool conditions illustrate that the separation strategy is robust and promising for cutting process monitoring.     

Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion

In a fully automated manufacturing environment, instant detection of the cutting tool condition is essential for the improved productivity and cost effectiveness. This paper studies a tool condition monitoring system (TCM) via machine learning (ML) and machine ensemble (ME) approach to investigate the effectiveness of multisensor fusion technique when machining 4340 steel with multilayer coated and multiflute carbide end mill cutter. In this study, 135 different features are extracted from multiple sensor signals of force, vibration, acoustic emission and spindle power in the time and frequency domain by using data acquisition and signal processing module. Then, a correlation-based feature selection technique (CFS) evaluates the significance of these features along with machining parameters collected from machining experiments. Next, an optimal feature subset is computed for various assorted combinations of sensors. Finally, machine ensemble methods based on majority voting and stacked generalization are studied for the selected features to classify not only flank wear but also breakage and chipping. It has been found in this paper that the stacked generalization ensemble can ensure the highest accuracy in tool condition monitoring. In addition, it has been shown that the support vector machine (SVM) outperforms other ML algorithms in most cases tested.     

Tool condition monitoring in turning using fuzzy set theory

This paper presents a study on tool condition monitoring in turning using the fuzzy set theory. The tool conditions considered include tool breakage, several states of tool wear, and chatter. Force, vibration, and power sensors are used in this study to monitor the three components of the cutting force, i.e. acceleration of the tool holder in two perpendicular directions, and the spindle motor current respectively. A total of 11 monitoring indices (signature features) are selected to describe the signature characteristics of various tool conditions. A linear fuzzy equation is proposed to describe the relationship between the tool conditions and the monitoring indices. The proposed methodology is verified experimentally using a total of 396 cutting tests performed at 52 different cutting conditions. The proposed methodology is also compared with that of several classification schemes, including the K-mean and the Fisher's pattern recognition methods, the nearest neighbor method and the fuzzy C-mean method. The results indicate an overall 90% reliability of the proposed methodology for detecting tool conditions regardless of the variation in cutting conditions.     

Performance of a new piezoceramic thick film sensor for measurement and control of cutting forces during milling

Process monitoring and controlling with detailed real-time information such as the cutting force is required for improving the performance of milling processes. This paper presents a novel system for measuring the cutting force in the direct vicinity of the indexable insert. The outlined concept makes use of piezoelectric thick film sensors mounted directly below the insert. The unique design and features of the new sensor enable accurate and continuous force measurements as well as the indication of wear. The results of the experimental investigations are presented and compared to results of a conventional dynamometer.     

Tooling-integrated sensing systems for stamping process monitoring

In-situ stamping process monitoring plays a critical role in enhancing productivity and ensuring part quality in sheet metal stamping. This paper investigates the realization of two sensing methods to create a tooling-integrated sensing system: mutual inductance-based displacement measurement for sheet draw-in, and distributed contact pressure measurement at the tool–workpiece interface. The two sensing systems are complementary in nature, and together, they significantly enhance the on-line observability of the stamping process. The performance of the draw-in sensor was evaluated using numerical simulations and experiments in a small-scale and a large-scale lab setup, and its effectiveness has been confirmed under the presence of wrinkled sheet. To study the spatial and temporal variations of the tool–workpiece contact pressure in a stamping operation, experiments were conducted on a customized panel stamping test-bed with an array of thin-film force sensors embedded below the die flange and die cavity. The force sensor data were then numerically interpolated to form the contact pressure distribution across the tool–workpiece interface, based on the thin plate spline (TPS) method. Comparison between the interpolated pressure obtained from the surface generation techniques and direct measurement using redundant sensors and a press mounted load cell confirms the validity of the new contact pressure sensing method. The integrated sensing technique provides insight into the stamping process by quantifying process variations and providing a reference base for process control to reduce product disparities. Additionally, new product and process designs can be created based on the quantified and referenced variations.     

Sweeping filters and tooth rotation energy estimation (TREE) techniques for machine tool condition monitoring

This paper presents a method for the condition monitoring of the milling cutting process based upon a combination of two techniques; sweeping filters and tooth rotation energy estimation (TREE). Existing spindle speed and spindle load signals from the machine are used thus avoiding the need for any additional sensors. The sweeping filter technique determines the frequency components of the spindle signal using low cost hardware. The filter's cut off frequency is swept across a range of frequencies and its output is acquired and analysed in real time. The variations of individual tooth energies estimated by the TREE technique in the time domain are used to verify the results. The hybrid approach created is based on the verification of any indicated faults before making a final conclusion about the health of the cutting tool. This provides a robust and reliable tool monitoring system that is able to identify tool breakage in real time during machining operations.     

影响高频电主轴工作寿命的主要原因及解决方法

对高频电主轴的发热和损耗状况进行了分析，针对影响电主轴．寿命的主要原因，提出了提高电主轴转子加工精度的措施和选择合理的电磁方案以降低温升；阐明了电主轴轴承结构的合理选择覆润滑方式。     

Monitoring of fibre reinforced composites with embedded optical fibre Bragg sensors, with application to filament wound pressure vessels

Tests carried out on bare optical fibres with a Bragg sensor show the feasibility of using these sensors for strain sensing. They have been embedded into simple composite laminates and have been subjected to static loading in bending tests. The measured strain from the Bragg sensor is perfectly linear with the applied force. Optical fibres with a Bragg sensor have also been embedded into filament wound pressure vessels. Tests carried out on such a pressure vessel include both static and slowly varying load schemes. The Bragg signal is nearly perfectly linear with the applied pressure. The results demonstrate the applicability of Bragg sensors for continuous monitoring of composite materials.     

基于深度学习和多传感器的数控机床铣刀磨损状态信号监测方法研究

由于单一传感器存在获取信息量有限、抗干扰能力较弱等问题及传统网络模型诊断时间长、诊断率低等现象,采用振动、噪声等多个传感器监测铣刀的磨损状态。提出将深度学习和多传感器相结合的铣刀磨损状态信号监测方法;将经核主元筛选和未筛选的数据分别输入到BP神经网络、RBF神经网络和深度卷积神经网络中进行模式识别,并对识别结果进行对比和分析。结果表明:深度学习和多传感器相结合的铣刀磨损状态监测方法在特征量比较大、数据量比较多的情况下诊断速度、准确率均比较高,在铣刀磨损状态监测中具有明显的优势。     

Strain field analysis and sensor design for monitoring machine tool spindle bearing force

The fluctuating strain field produced by the rolling motion of the spindle bearing is analyzed by an elastic model and verified with experimental data. This strain field analysis is of considerable practical significance because of its close correlation to spindle bearing preload, cutting forces, and bearing running conditions. Based on the model, a conventional sensing scheme with strain gages mounted in a groove ground around the bearing outer ring is optimized by selecting proper sensor sizes, locations, and configurations such that signal cross-over error is minimized. In addition, the feasibility of a non-invasive sensing scheme achieved by attaching high sensitivity sensors on the outside surface of the spindle housing is studied. From the strain model, it is found that the level of strain field at the housing surface is substantially lower, and its distribution is not concentrated. Therefore, high sensitivity sensors and different sensing schemes are needed. Simulation results show that, compared with the conventional scheme, the output of this scheme requires less signal processing when the force acting on the bearing is fluctuating.     

外圆磨削颤振监测方法设计

磨削颤振是影响加工质量和加工效率的重要因素,常需增加多个后续加工周期来满足加工质量的要求。采用涡流传感器、磨削力传感器、声发射传感器、加速度传感器和接触探头等多传感器对砂轮边沿的自激振荡进行测量,对自激振荡信号,提出采用幅值法和小波分析法进行分析处理,实现了磨削颤振的自动监测和预测,减少了加工周期,提高了加工效率。