Real‐Time Monitoring of Tool Fracture in Turning Using Sensor Fusion

The sensor fusion method using both an acoustic emission (AE) sensor and a built-in force sensor is introduced for on-line tool condition monitoring during turning. The cutting force was measured by a built-in piezoelectric force sensor, which was inserted in the tool turret housing of an NC lathe. FEM analysis was carried out to locate the most sensitive position for the sensor. A burst of AE signal was used as a triggering signal to inspect the cutting force. A significant drop in cutting force indicated tool breakage. The algorithm was implemented in a DSP board and the monitoring system was installed on a CNC lathe in an FMS line for in-process tool-breakage detection. The proposed system showed an excellent monitoring capability.     

Workpiece roundness profile in the frequency domain: an application in cylindrical plunge grinding

In grinding, most control strategies are based on the spindle power measurement, but recently, acoustic emission has been widely used for wheel wear and gap elimination. This paper explores a potential use of acoustic emission (AE) to detect workpiece lobes. This was achieved by sectioning and analysing the AE signal in the frequency domain. For the first time, the profile of the ground workpiece was predicted mathematically using key frequencies extracted from the AE signals. The results were validated against actual workpiece profile measurements. The relative shift of the wave formed on the surface of the part was expressed using the wheel-workpiece frequency ratio. A comparative study showed that the workpiece roundness profile could be monitored in the frequency domain using the AE signal during grinding.     

Investigation on the effect of EDM process variables and environments on acoustic emission signals

Abstract

The performance of electrical discharge machining (EDM) primarily depends on the spark quality generated in the inter-electrode gap (IEG) between the tool and workpiece. A method for obtaining accurate information about the spark gap is required to effectively monitor the EDM process. The rise and fall of thermal energy in the discharge zone at a rapid rate during the dielectric breakdown produces high-pressure shock waves. This work explores the suitability of using acoustic emission (AE) generated from these shock waves and the elastic AE waves released on the workpiece due to the induced stress to monitor the performance and spark gap in EDM. The information content of the AE signals acquired at various machining conditions was extracted using AE RMS, spectral energy and peak amplitude. These features were able to well discriminate the machining condition, tool material, workpiece material, flushing pressure, current density, the initial surface roughness of the tool. Additionally, the AE signal features had a good and consistent correlation with the performance parameters, including material removal rate, surface roughness (Ra and Rq) and tool wear. The findings lay the groundwork to develop an effective, non-intrusive in-situ AE-monitoring system for performance and IEG condition in EDM.     

Model development for tool wear effect on AE signal generation in micromilling

A model for the relation between the acoustic emission signal generation and tool wear was established for cutting processes in micromilling by considering the acoustic emission (AE) generation and propagation mechanisms. In addition, the effect of tool wear on the AE signal generation in frequency and amplitude was studied. In the model development, the finite element analysis was first used to calculate the shear strain rate distribution on the shear plane based on the orthogonal cutting assumption. Conversely, the contact stress distribution of workpiece on the flank wear face was established based on the Waldorf model. Following the finite element method, the dislocation density in materials was calculated based on Orowan’s law with the calculated stress rate. Finally, the AE signal detected by the sensor was calculated by considering the Gaussian probability density function for the distribution of AE source on the shear plane and the one-dimension wave equation for AE signal propagation. Based on the developed model, the effect of tool wear on the AE signal generation was investigated and compared to the experimental results. The results obtained from these investigations indicate that the proposed model can be used to predict the effect of tool wear on the AE signal generation.     

基于FANUC 31i的主轴热变形补偿方法

主轴高速旋转时,主轴轴承内外环高速摩擦产生大量热量,这些热量使主轴轴向和空间姿态发生变化,产生热伸长、热倾斜和热漂移等形变,这些形变又引起刀具与工件相对位置发生变化,导致工件加工精度变差。采用五点测量法对这些形变量进行测量,生成主轴温升与热变形的误差曲线,再根据误差曲线编制数控系统可执行的C语言热补偿程序或PMC热补偿程序,数控系统根据温差变化自动更新外部机械原点偏移量,纠正刀具与工件的相对位置偏差,可有效减小主轴热变形引起的误差,提高工件加工精度。     

A study of tool wear using statistical analysis of metal-cutting acoustic emission

Many aspects of the interactions between cutting tools, workpiece material and the chips formed during machining that affect the wear and failure of the tool are not fully understood. The analysis of acoustic emission signals generated during machining has been proposed as a technique for studying both the fundamentals of the cutting process and tool wear and as a methodology for detecting tool wear and failure on line. A brief review of the theory of acoustic emission is presented. Acoustic emission data from reduced contact length machining experiments and tool flank wear tests are analyzed using distribution moments. The analysis shows that the skew and kurtosis of an assumed β distribution for the r.m.s. acoustic emission signal are sensitive to both the stick-slip transition for chip contact along the tool rake face and progressive tool wear on the flank of the cutting tool.     

Investigating the cutting phenomena in free-form milling using a ball-end cutting tool for die and mold manufacturing

This paper presents an investigation of nonplanar tool-workpiece interactions in free-form milling using a ball-end cutting tool, a technique that is widely applied in the manufacturing of dies and molds. The influence of the cutting speed on the cutting forces, surface quality of the workpiece, and chip formation was evaluated by considering the specific alterations of the contact between tool-surface along the cutting time. A trigonometric equation was developed for identifying the tool-workpiece contact along the toolpath and the point where the tool tip leaves the contact with the workpiece. The experimental validation was carried out in a machining center using a carbide ball-end cutting tool and a workpiece of AISI P20 steel. The experimental results demonstrated the negative effect of the engagement of the tool tip into the cut on machining performance. The length of this engagement depends on the tool and workpiece curvature radii and stock material. When the tool tip center is in the cut region, the material is removed by shearing together with plastic deformation. Such conditions increase the cutting force and surface roughness and lead to an unstable machining process, what was also confirmed by the chips collected.     

Development of a rapid profilometer with an application to roundness gauging

This paper presents the development of a real-time, contact-based, high frequency, response profilometer employed as a roundness gauge for measuring circular form error for 100% part inspection on the shop floor with an accuracy of better than 0.5 μm with measurement times of less than 1 s. The gauge head is a closed-loop controlled mechanism comprising a contact force probe that is rigidly attached to a high bandwidth linear translator. The gauge head assembly is, in turn, attached to a precision spindle. The objective of the complete tool is to contact the sidewall of the circular feature, translate the probe tip to produce a defined contact force with the workpiece and rotate the gauge head assembly. During rotation of the spindle, this contact force is maintained at a nominally constant value by dynamically translating the force probe along a radial direction to follow surface deviations. The gauge head assembly (including force probe and servo drive) has a fundamental mode natural frequency of 330 Hz while scanning the workpiece with a constant contact force typically less than 100 mN. Form error (deviation from a nominal circle) is measured using a capacitance-based displacement sensor measuring the relative radial displacement of the probe with the spindle rotating at a constant rotation speed. This paper discusses the design and characterization of this metrology tool.     

Monitoring of hard turning using acoustic emission signal

Monitoring of tool wear during hard turning is essential. Many investigators have analyzed the acoustic emission (AE) signals generated during machining to understand the metal cutting process and for monitoring tool wear and failure. In the current study on hard turning, the skew and kurtosis parameters of the root mean square values of AE signal (AERMS) are used to monitor tool wear. The rubbing between the tool and the workpiece increases as the tool wear crosses a threshold, thereby shifting the mass of AERMS distribution to right, leading to a negative skew. The increased rubbing also led to a high kurtosis value in the AERMS distribution curve.     

Monitoring Strategy of Thermal-Induced Convex Deformation of Workpiece in Surface Grinding

In surface grinding, the presence within the workpiece of a high temperature gradient causes a large thermal bending moment, which can cause over-grinding due to the induced convex deformation of the workpiece by the bending moment. The average grinding forces and acoustic emission (AE) are used to predict the over-grinding, based on the facts that the up-grinding force is larger than the down-grinding force, and the AE is larger in up-grinding. Hence, over-grinding can be detected between passes from the mean values of the grinding forces and AE during grinding. Because of the positional separation between the bottom of the workpiece and the dynamometer, the signal transmission from the contact zone to the transducer is reduced, and so the AE does not satisfactorily indicate the amount of over-grinding. The strategy for monitoring the over-grinding by measuring the grinding force is satisfactorily verified in this study.     

EQUIVALENT NORMAL CURVATURE APPROACH MILLING MODEL OF MACHINING FREEFORM SURFACES

A new milling methodology with the equivalent normal curvature milling model machining freeform surfaces is proposed based on the normal curvature theorems on differential geometry. Moreover, a specialized whirlwind milling tool and a 5-axis CNC horizontal milling machine are introduced. This new milling model can efficiently enlarge the material removal volume at the tip of the whirlwind milling tool and improve the producing capacity. The machining strategy of this model is to regulate the orientation of the whirlwind milling tool relatively to the principal directions of the workpiece surface at the point of contact, so as to create a full match with collision avoidance between the workpiece surface and the symmetric rotational surface of the milling tool. The practical results show that this new milling model is an effective method in machining complex three- dimensional surfaces. This model has a good improvement on finishing machining time and scallop height in machining the freeform surfaces over other milling processes. Some actual examples for manufacturing the freeform surfaces with this new model are given.     

用于加工中心的在线工件自动测量系统

介绍一种可用于加工中心的在线工件自动测量系统的构成及功能。该系统采用新型触发式传感器 ,可使刀具刀尖自身作为测头测试工件加工尺寸。该系统可用于工件的装夹找正 ,也可对工件进行自动在线测量 ,并能根据测量结果对加工误差进行补偿以提高加工精度     

Determining the cutting conditions for sculptured surface machining

This paper deals with a new method for determining cutting conditions for the machining of 3D sculptured surface shapes with a ball endmill. Since the interaction area between workpiece and cutting tool changes during ball endmill machining, the cutting conditions have to be changed accordingly. To determine the spindle speed, the tool-workpiece contact area is obtained from the depth of cut, path interval, and cutter contact (CC) data. The spindle speed is determined so that the tangential cutting speed of the centroid of the tool-workpiece contact area becomes the value suggested in the machining data handbook. The feedrate is determined so that the material removal rate is maximised within the range of the cutting-force constraint. The practical feasibility of the proposed scheme has been verified through experiments performed on a workpiece with sculptured surfaces. The test result shows that the new method can be used to increase the tool life, and improve the production rate.     

IN-PROCESS TOOL CONDITION MONITORING USING ACOUSTIC EMISSION SENSOR IN MICROENDMILLING

Recently researchers and manufacturers have shown keen interest in fabricating micro-components through tool based mechanical micromachining processes namely micromilling, microdrilling, microturning, etc. In this scenario, microendmilling is used in the manufacture of micro-molds, micro-dies, micro-channel, micro-gear, etc. The major issue in microendmilling process is the unpredictable life of the micro-tool and its premature failure during operations. Therefore in this work, an attempt has been made to monitor the tool condition (in-process) using acoustic emission (AE) sensor in microendmilling of different materials such as aluminum, copper and steel alloys. From this study, it is observed that there is a strong relationship between the tool wear (flank wear) and acoustic emission (AE_RMS) signals, surface roughness (R_a) as well as chip morphology. In order to understand the mechanism of tool wear, SEM and EDAX analyses were carried out on the microendmill after machining. Scanning Electron Microscope (SEM) and energy dispersive X-ray spectroscopy (EDAX) analyses indicated occurrence of the tool wear mechanism such as adhesion and plastic deformation in all three materials. Coating delamination is also observed while machining steel alloy. This work provides significant and new knowledge on the usage of AE sensor in monitoring the tool condition and understanding the tool wear mechanism in microendmilling of different materials.     

Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling

This paper presents an online prediction of tool wear using acoustic emission (AE) in turning titanium (grade 5) with PVD-coated carbide tools. In the present work, the root mean square value of AE at the chip–tool contact was used to detect the progression of flank wear in carbide tools. In particular, the effect of cutting speed, feed, and depth of cut on tool wear has been investigated. The flank surface of the cutting tools used for machining tests was analyzed using energy-dispersive X-ray spectroscopy technique to determine the nature of wear. A mathematical model for the prediction of AE signal was developed using process parameters such as speed, feed, and depth of cut along with the progressive flank wear. A confirmation test was also conducted in order to verify the correctness of the model. Experimental results have shown that the AE signal in turning titanium alloy can be predicted with a reasonable accuracy within the range of process parameters considered in this study.     

Nonlinear adaptive backstepping control for variable-speed wind energy conversion system-based permanent magnet synchronous generator

Tool wear degradation and working status of slotting cutter have a great effect on the surface quality of rotor slot; therefore, tool condition monitoring and its degradation estimation are needed for guaranteeing slot machining quality. This paper proposes a two-phase method based on acoustic emission (AE) signal classification and logistic regression model for slotting cutter condition monitoring and its degradation estimation. In the first phase, the failure reliability estimation models corresponding to different machining processes are established considering the variability of process system like tool regrinding times and material randomness of workpiece. In the second phase, the most appropriate estimation model corresponding to the optimum cluster is selected and used for failure reliability estimation and status determination of slotting cutter. This approach has been validated on a CNC rotor slot machine in a factory. Experimental results show that the proposed method can be effectively used for cutting tool degradation estimation and status determination of slotting cutter with high accuracy.     

FLUID MECHANICS APPROACH TO MACHINING AT HIGH SPEEDS: PART II: A POTENTIAL FLOW MODEL

Finite element models of machining at high speeds usually assume that there is a stagnation point at the tool tip as is the norm in the machining community. However, at ultra-high speeds the yield strength of the workpiece is easily exceeded in the material around the tool tip, allowing that material to “flow” and possibly allowing the stagnation point to migrate away from the tool tip. A potential flow solution is used to model the behavior of the material around a sharp tool tip during machining at high speeds. Interestingly, the flow solution predicts that there is a stagnation point on the rake face, not at the tool tip as is usually assumed. Because the stagnation point is not at the tool tip, the flow solution predicts a significant amount of deformation in the workpiece resulting in large residual strains and a possible related temperature rise on the finished surface.     

Energy Dissipation Control in Hydro-Abrasive Machining Using Quantitative Acoustic Emission

The efficiency and product quality of hydro-abrasive machining (HAM) could be considerably improved if the energy dissipation phenomenon of the jet-like tool during the material removal is clearly understood. An on-line technique for quantifying the amount of energy dissipated in the workpiece during HAM using acoustic emission (AE) measurements is presented in this paper. The measured AE-signals are linked to a physical energy dissipation model. Stochastic modelling of the AE-signals provides more information on the physics of the energy dissipation process.     

基于BP神经网络的表面粗糙度声发射预测

介绍了BP神经网络的原理、算法和公式,在对Matlab及其神经网络工具箱介绍的基础上,采用声发射信号有效值、FFT峰值和标准差作为输入,工件表而粗糙度作为输出,用BP神经网络的方法对高效深磨加工工程陶瓷Al2O3的工件表面粗糙度进行了训练、预测和分析.创新的研究方法是直接从磨削声发射信号中提取磨削表面粗糙度信息.结果表明,该方法可以实现高效深磨加工工程陶瓷工件表面粗糙度的监测.     

Reduction of Air-Grinding Time Using Collaboration of Dual Sensors

Air-grinding time is inevitable in the grinding process. Since the low feedrate during air grinding makes the process inefficient, air-grinding time should be reduced. This paper presents a technology for reducing air-grinding time in cylindrical plunge grinding operations. The main idea is to estimate a distance, NAP (nearest approaching point), between the workpiece surface and the position of the grinding wheel. After detecting the NAP using an ultrasonic sensor, a CNC controller adjusts the feedrate using the feedrate override function with the help of the AE signal that gives the CP (contact point) information. The experimental results show that an ultrasonic and an AE sensor are good enough for detecting the NAP and the CP, respectively, and the system reduces the conventional air-grinding time by two-thirds.