Novel sensor-based tool wear monitoring approach for seamless implementation in high speed milling applications

A sensor-based hybrid processing approach for tool wear monitoring is presented to overcome the practical limitations of implementing state-of-the-art tool condition monitoring systems in milling processes. It extracts features from vibration signals that are insensitive to the variations in cutting conditions, tool path and interfering noises. A machine learning model was developed to accentuate features separation based on tool condition. Extensive experimental validation tests in high speed and conventional milling applications demonstrated the approach capability to achieve 98% accuracy and reduce system training by up to 97%. Such performance, practicality and accuracy have never been reached before in this application.     

An investigation of methods for the on-line estimation of tool wear

The paper examines the use of the measurements of spindle motor power for the estimation of wear and the detection of the end of effective tool life for a vertical milling machine. The measurements are analysed using spectral analysis and the effectiveness of using moving averages, running-means and cumulative sum of the power spectrum values to estimate wear is evaluated. For a specific machine tool (Beaver NC5), workpiece material (stainless steel) and under a particular set of cutting conditions it is shown that (a) there is a strong correlation between the cumulative sum of the power spectral energies and the total flank wear, and (b) the residuals obtained from computing a least squares linear fit to the plot of cumulative sums of the power spectral energies against cutting time clearly indicates when the tool is entering the region of high wear.     

Prediction of chatter in NC machining based on a dynamic cutting force model for ball end milling

Ball end milling is one of the most widely used cutting processes in the automotive, aerospace, die/mold, and machine parts industries, and the chatter generated under unsuitable cutting conditions is an extremely serious problem as it causes excessive tool wear, noise, tool breakage, and deterioration of the surface quality. Due to the critical nature of detecting and preventing chatter, we propose a dynamic cutting force model for ball end milling that can precisely predict the cutting force for both stable and unstable cutting states because our uncut chip thickness model considers the back-side cutting effect in unstable cutting states. Furthermore, the dynamic cutting force model considers both tool runout and the penetration effect to improve the accuracy of its predictions. We developed software for calculating the cutting configuration and predicting the dynamic cutting force in general NC machining as well as single-path cutting. The chatter in ball end milling can be detected from the calculated cutting forces and their frequency spectra. A comparison of the predicted and measured cutting forces demonstrated that the proposed method provides accurate results.     

Predictive model of tool wear in milling with coated tools integrated into a CAM system

The coated tool wear evolution in milling at constant cutting conditions can be described analytically based among other factors on the cutting edge entry impact duration. A tool wear predictive mathematical model for milling parts of complicated geometry was created employing this methodology and a commercial CAM system. Parameters of the developed model were determined based on experimental results. In this way, the expected tool wear growth during numerically controlled milling can be estimated, considering the cutting penetrations along the tool paths, the process up or down kinematic and other factors. The application of the introduced model is demonstrated through appropriate examples.     

An overview of approaches to end milling tool monitoring

The increase in awareness regarding the need to optimise manufacturing process efficiency has led to a great deal of research aimed at machine tool condition monitoring. This paper considers the application of condition monitoring techniques to the detection of cutting tool wear and breakage during the milling process. Established approaches to the problem are considered and their application to the next generation of monitoring systems is discussed. Two approaches are identified as being key to the industrial application of operational tool monitoring systems.Multiple sensor systems, which use a wide range of sensors with an increasing level of intelligence, are seen as providing long-term benefits, particularly in the field of tool wear monitoring. Such systems are being developed by a number of researchers in this area. The second approach integrates the control signals used by the machine controller into a process monitoring system which is capable of detecting tool breakage. Initial findings mainly under laboratory conditions, indicate that both these approaches can be of major benefit. It is finally argued that a combination of these approaches will ultimately lead to robust systems which can operate in an industrial environment.     

基于切削力的铣削加工误差数学模型

针对铣削加工过程中由于工件、设备系统弹性变形以及刀具磨损导致的加工误差进行了分析和研究.提出了以切削分力作为主要参数的由于刀具磨损和工件与机械系统的弹性变形所带来的加工误差的数学模型.     

Tool breakage detection using support vector machine learning in a milling process

In this paper, an intelligent tool breakage detection system which uses a support vector machine (SVM) learning algorithm is proposed to provide the ability to recognize process abnormalities and initiate corrective action during a manufacturing process, specifically in a milling process. The system utilizes multiple sensors to record cutting forces and power consumptions. Attention is focused on training the proposed system for performance improvement and detecting tool breakage. Performance of the developed system is compared to the results from an alternative detection system based on a multiple linear regression model. It is expected that the proposed system will reduce machine downtime, which in turn will lead to reduced production costs and increased customer satisfaction.     

Experimental studies of cutting force variation in face milling

The purpose of this paper is to present a developed cutting force model for multi-toothed cutting processes, including a complete set of parameters influencing the cutting force variation that has been shown to occur in face milling, and to analyse to what extent these parameters influence the total cutting force variation for a selected tool geometry. The scope is to model and analyse the cutting forces for each individual tooth on the tool, to be able to draw conclusions about how the cutting action for an individual tooth is affected by its neighbours.A previously developed cutting force model for multi-toothed cutting processes is supplemented with three new parameters; eccentricity of the spindle, continuous cutting edge deterioration and load inflicted tool deflection influencing the cutting force variation. A previously developed milling force sensor is used to experimentally analyse the cutting force variation, and to give input to the cutting force simulation performed with the developed cutting force model.The experimental results from the case studied in this paper show that there are mainly three factors influencing the cutting force variation for a tool with new inserts. Radial and axial cutting edge position causes approximately 50% of the force variation for the case studied in this paper. Approximately 40% arises from eccentricity and the remaining 10% is the result of spindle deflection during machining. The experimental results presented in this paper show a new type of cutting force diagrams where the force variation for each individual tooth when two cutting edges are engaged in the workpiece at the same time. The wear studies performed shows a redistribution of the individual main cutting forces dependent on the wear propagation for each tooth.     

Theoretical-experimental modeling of milling machines for the prediction of chatter vibration

Productivity of high speed milling operations can be seriously limited by chatter occurrence. Chatter vibrations can imprint a poor surface finish on the workpiece and can damage the cutting tool and the machine. Chatter occurrence is strongly affected by the dynamic response of the whole system, i.e. the milling machine, the tool holder, the tool, the workpiece and the workpiece clamping fixture. Tool changes must be taken into account in order to properly predict chatter occurrence. In this study, a model of the milling machine-tool is proposed: the machine frame and the spindle were modeled by an experimentally evaluated modal model, while the tool was modeled by a discrete modal approach, based on the continuous beam shape analytical eigenfunctions. A chatter identification technique, based on this analytical-experimental model, was implemented. Tool changes can be easily taken into account without requiring any experimental tests. A 4 axis numerically controlled (NC) milling machine was instrumented in order to identify and validate the proposed model. The milling machine model was excited by regenerative, time-varying cutting forces, leading to a set of Delay Differential Equations (DDEs) with periodic coefficients. The stability lobe charts were evaluated using the semi-discretization method that was extended to n>2 degrees of freedom (dof) models. The stability predictions obtained by the analytical model are compared to the results of several cutting tests accomplished on the instrumented NC milling machine.     

A new method of cutting force measurement based on command voltages of active electro-magnetic bearings

In this paper, a new indirect method of measuring dynamic cutting forces is proposed. Milling tests have been performed on a five-axis machine, Gambin 120CR, fitted out with an electro-spindle with magnetic bearings developed by the company S2M, and named SMB30. These bearings are not affected by friction and wear. An experimental approach has been developed to determine the cutting forces as a function of the measured command voltages of the milling spindle’s magnetic bearings. The spindle is treated as a “black box”, where the transfer functions linking the unknown cutting force with command voltages are established experimentally. The cutting forces calculated from the command voltages of magnetic bearings are in good agreement with the ones measured with a Kistler four-component dynamometer. This indirect method of cutting force determination provides a useful way to estimate tool wear and monitor product quality in high-speed milling on-line.     

The form error prediction in side wall machining considering tool deflection

In this research, an effective method for the form error prediction in side wall machining with a flat end mill is suggested. The form error is predicted directly from the tool deflection without surface generation by cutting edge locus with time simulation. The developed model can predict the surface form error accurately about 300 times faster than the previous method. Cutting forces and tool deflection are calculated considering tool geometry, tool setting error and machine tool stiffness. The characteristics and the difference of generated surface shape in up milling and down milling are discussed. The usefulness of the presented method is verified from a set of experiments under various cutting conditions generally used in die and mold manufacturing. This study contributes to real time surface shape estimation and cutting process planning for the improvement of form accuracy.     

SKD11铣削刀具寿命试验研究及工艺参数优化

为提高SKD11模具钢铣削刀具的寿命,对SKD11模具钢进行了刀具寿命铣削试验,基于极差方法分析了各工艺参数对刀具寿命的影响规律。基于刀具寿命铣削试验,利用多元线性回归方法,推导并求解出了SKD11模具钢铣削刀具磨损的数学模型。利用最优化设计方法和MATLAB优化工具箱,以加工效率和刀具磨损为目标函数,针对实际的铣削问题优选了工艺参数。优化的工艺参数能兼顾刀具寿命和加工效率,为加工企业降低综合生产成本提供了重要的理论依据和案例参考。     

Modelling and simulation of chatter in milling using a predictive force model

A predictive time domain chatter model is presented for the simulation and analysis of chatter in milling processes. The model is developed using a predictive milling force model, which represents the action of milling cutter by the simultaneous operations of a number of single-point cutting tools and predicts the milling forces from the fundamental workpiece material properties, tool geometry and cutting conditions. The instantaneous undeformed chip thickness is modelled to include the dynamic modulations caused by the tool vibrations so that the dynamic regeneration effect is taken into account. Runge–Kutta method is employed to solve the differential equations governing the dynamics of the milling system for accurate solutions. A Windows-based simulation system for chatter in milling is developed using the predictive model, which predicts chatter vibrations represented by the tool-work displacements and cutting force variations against cutter revolution in both numerical and graphic formats, from input of tool and workpiece material properties, cutter parameters, machine tool characteristics and cutting conditions. The system is verified with experimental results and good agreement is shown.     

An investigation of cutting forces in machining with worn ball-end mill

Reliable tool wear monitoring technique is one of the important aspects for achieving an integrated and self-adjusting manufacturing system. In this study, an analytical model is proposed to estimate the cutting forces, the tool geometry, and the chip geometry in relation to the flank wear, when milling with a ball-end mill. Modeling is based on thermomechanical modelling of oblique cutting. The worn tool geometry is decomposed into a series of axial elementary cutting edges. At any active tooth element, the flank wear geometry is calculated and the chip formation is obtained from an oblique cutting process characterised by local undeformed chip section and local cutting angles. Coated carbide ball-end tool, and a titanium workpiece material have been considered in this paper. The results found by using developed models have shown good agreement with experimental results.     

Method for determining edge chipping in milling based on tool holder vibration measurements

Process monitoring and in-process control in milling requires reliable sensor data from rotating tooling systems. Vibration sensing by an instrumented tool holder close to the cutting zone has been proven useful in mitigating chatter. A model is presented that enables the interpretation of vibration data of the rotating sensor and tool system to identify edge chipping. Thus, data from a single axis accelerometer can be interpreted to identify the existence of edge chipping on one of multiple cutting edges. The classification of observed effects is derived from methods developed for machine learning, which is demonstrated and evaluated by experimental results.     

High-speed milling of titanium alloys using binderless CBN tools

The performance of conventional tools is poor when used to machine titanium alloys. In this paper, a new tool material, which is binderless cubic boron nitride (BCBN), is used for high-speed milling of a widely used titanium alloy Ti–6Al–4V. The performance and the wear mechanism of the BCBN tool have been investigated when slot milling the titanium alloy in terms of cutting forces, tool life and wear mechanism. This type of tool manifests longer tool life at high cutting speeds. Observations based on the SEM and EDX suggest that adhesion of workpiece and attrition are the main wear mechanisms of the BCBN tool when used in high-speed milling of Ti–6Al–4V.     

Design and implementation of tuned viscoelastic dampers for vibration control in milling

Passive means of vibration attenuation have been employed successfully and efficiently in machining systems such as turning and milling. Traditional approach to controlling vibration in a milling system is to develop control mechanisms for cutting tools or machine spindles. However, due to the nature of milling operations where the cutting tools rotate at high speed, the passive vibration control methods find very limited application with the traditional approach. In order to utilise the potential of the passive vibration control methodology in milling applications, the milling operation should be viewed as a system comprising an elastic structure and operation parameters. Dynamics of this closed-loop system should improve with improvement in dynamics of any of the system components, especially within the elastic structure that comprises the cutting tool, the machine tool, the workholding system and the workpiece. Although the level of improvement will vary depending on which component of the elastic chain is targeted for this purpose. This paper presents the development and testing of tuned viscoelastic dampers (TVDs) for vibration control through their application on a workpiece in milling operations. This work targets workpiece held on a palletised workholding system for the control of unwanted vibration and thus deviates from the traditional approach where cutting tool and/or machine spindles are targeted for vibration control strategies. Palletised workholding systems, due to their compact design, offer an opportunity to design passive damping mechanisms that are easier to implement in the case of a milling system. The TVD developed through this research is based on a commercially available viscoelastic damping polymer. Advantage of such materials is their high damping performance over a wide range of excitation frequencies. The TVD design process has used a unique combination of analytical modelling with experimental FRF data. Modal impact testing showed that the application of the TVD reduced the amplitude of vibration acceleration by 20 dB for the target mode. Since the target mode corresponded to torsional vibration, the TVD was effective in two planar coordinates, i.e. X and Y. In addition, the TVD also significantly reduced the amplitude of a vibration mode far from the mode it was designed for. The system has been tested experimentally to demonstrate significant reduction in vibration amplitudes during a milling process. The milling tests with different combinations of cutting parameters show that multi-TVD approach is always valid regardless of the parameters being used. The only requirement for TVDs to function effectively is that the natural frequency of the system, for which the TVDs are designed, is excited during the milling process.     

A systems theory approach to modelling of cutting temperature with experimental identification

A parametric model of the thermal process generated in metal cutting is developed in a theoretical system approach. A family of transfer functions of a piecewise linear system is provided for analysing both the transient and the steady-state response of the cutting temperature to inputs of cutting speeds. The influence of tool wear on the raising of the cutting temperature is incorporated in the global sensitivity of the model parameters towards the working conditions. A standard thermocouple embedded in the tool holder is used to monitor the thermal process in continuous and interrupted turning experiments. A model assessment based on an analysis of parameter estimates is obtained after an extensive identification procedure. The model can be referred to for implementing real-time control of the cutting process.     

Application of statistical filtering for optical detection of tool wear

The application of automated tool condition monitoring systems is very important for unmanned machining systems. Tool wear monitoring is a key factor for optimization of the cutting processes. Basically, tool wear monitoring systems can be subdivided into two classes: direct and indirect. Currently direct tool wear monitoring systems are most frequently based on machine vision by camera. Several approaches have been studied for tool wear detection by means of tool images, and an innovative statistical filter proved to be very efficient for worn area detection. A new approach has been implemented and tested in order to develop an automatic system for tool wear measurement. This new approach is described in this paper and the main topics related to tool wear monitoring using wear images have been discussed.     

Model-based predictive force control in milling: determination of reference trajectory

Today, powerful process simulation tools allow an offline process planning and optimization of metal cutting processes. The quality of the optimization strongly depends on the model and its parameters. Real cutting processes are influenced by uncertainties such as tool wear status or material properties, which are both unknown. To overcome this limitation, sensors and process control systems are used. Model-based Predictive Control (MPC) was developed in the 1970s for the chemical process industry. This control method was found to be very suitable to control complex manufacturing processes such as milling processes. Using MPC in metal cutting processes allows considering technological boundary conditions explicitly. Adapting the feed velocity and thus the process force increases the productivity and process stability in milling. A core element of the MPC is the use of a reference trajectory representing the time-dependent set point value in the optimization procedure. The tool path information, however, is given position-based. Thus, calculating the reference trajectory is not trivial and strongly influences the control quality. This paper presents two methods for determining the reference trajectory. The first method is based on an adaptive signal filter. For the second method the MPC is extended to a two-layer MPC: the first layer calculates an optimal reference trajectory; the second layer controls the machine tool.