A cutting power model for tool wear monitoring in milling

This paper describes a cutting power model in face milling operation, where cutting conditions and average tool flank wear are taken into account. The cutting power model is verified with experiments. It is shown with the simulations and experiments that the simulated power signals predict the mean cutting power better than the instantaneous cutting power. Finally, the cutting power model is used in a cutting power threshold updating strategy for tool wear monitoring which has been carried out successfully in milling operations under variable cutting conditions.     

TWEM, a method based on cutting forces—monitoring tool wear in face milling

One of the most important objectives in manufacturing is the intelligent machining system. To come to such a solution, the tool wear has to be determined on-line during the cutting process on unmanned machining systems. This contribution discusses the results experimentally obtained in face milling with a new rotating dynamometer. The paper introduces the concept of tool wear indicators which can be determined by simple analysis of the feature parameters of cutting force signals. The disturbance of the cutting force signals obtained by using the rotating dynamometer can be solved by applying tool wear indicators such as Normalized Cutting Force indicator (NCF) and Torque-Force Distance indicator (TFD). The Method for Tool Wear Estimation—TWEM is proposed.     

Driver current analysis for sensorless tool breakage monitoring of CNC milling machines

Tool wear and breakage detection is one of the most important problems found during manufacture in automated CNC machines. From several techniques devoted to sense tool condition, driver current monitoring has been used for a sensorless approach. In order to efficiently use the driver current monitoring technique an exhaustive analysis on the nature of the real components of the signal is required. The novelty of this paper is to present a driver current signal analysis to estimate the influence of the most important spurious signal components in order to determine the optimal parameters for signal conditioning. Beside the cutting force signal, the spurious signals considered in the analysis are high-frequency noise, current control commutation and ball screw effects. The analysis is compared with experimental data in order to validate the model and a case study is presented to show the general procedure.     

Real-time tool wear monitoring in milling using a cutting condition independent method

This paper describes a new method to monitor end milling tool wear in real-time by tracking force model coefficients during the cutting process. The behavior of these coefficients are shown to be independent from the cutting conditions and correlated with the wear state of the cutting tool. The tangential and radial force model coefficients are normalized and combined into a single parameter for wear monitoring. A number of experiments with different workpiece materials are run to investigate the feasibility of tool wear monitoring using this method. We show that this method can be used in real-time to track tool wear and detect the transition point from the gradual wear region to the failure region in which the rate of wear accelerates.     

Real time monitoring of tool breakage in a milling operation using a digital signal processor

A monitoring system that can detect tool breakage and chipping in real time was developed using a digital signal processor (DSP) board in a face milling operation. An autoregressive (AR) model and a band energy method were used to extract the features of tool states from cutting force signals. Then, two artificial neural networks, which have a parallel processing capability, were embedded on the DSP board to discriminate different malfunction states from features obtained by each of the two methods of signal processing. In experiments, we found that feature parameters extracted by AR modeling were more accurate indicators of malfunctions in the process than those from the band energy method, although the computing speed is slower. By using the selected features, we were able to monitor malfunctions in real time.     

A new versatile in-process monitoring system for milling

Tool condition monitoring (TCM) systems can improve productivity and ensure workpiece quality, yet, there is a lack of reliable TCM solutions for small-batch or one-off manufacturing of industrial parts. TCM methods which include the characteristics of the cut seem to be particularly suitable for these demanding applications. In the first section of this paper, three process-based indicators have been retrieved from literature dealing with TCM. They are analysed using a cutting force model and experiments are carried out in industrial conditions. Specific transient cuttings encountered during the machining of the test part reveal the indicators to be unreliable. Consequently, in the second section, a versatile in-process monitoring method is suggested. Based on experiments carried out under a range of different cutting conditions, an adequate indicator is proposed: the relative radial eccentricity of the cutters is estimated at each instant and characterizes the tool state. It is then compared with the previous tool state in order to detect cutter breakage or chipping. Lastly, the new approach is shown to be reliable when implemented during the machining of the test part.     

Data fusion neural network for tool condition monitoring in CNC milling machining

Several data fusion methods are addressed in this research to integrate the detected data for the neural network applications of on-line monitoring of the tool condition in CNC milling machining. One dynamometer and one accelerometer were used in the experiments. The collected signals were pre-processed to extract the feature elements for the purpose of effectively monitoring the tool wear condition. Different data fusion methods were adopted to integrate the obtained feature elements before they were applied into the learning procedure of the neural networks. The training-efficiency and test-performance of the data fusion methods were then analyzed. The convergence speed and the test error were recorded and used to represent the training efficiency and test performance of the different data fusion methods. From an analysis of the results of the calculations based on the experimental data, it was found that the performance of the monitoring system could be significantly improved with suitable selection of the data fusion method.     

Measurement of milling tool vibrations during cutting using laser vibrometry

Spindle and tool vibration measurements are of great importance in both the development and monitoring of high-speed milling. Measurements of cutting forces and vibrations on the stationary spindle head is the most used technique today. But since the milling results depend on the relative movement between the workpiece and the tool, it is desirable to measure on the rotating tool as close to the cutters as possible. In this paper the use of laser vibrometry (LDV) for milling tool vibration measurements during cutting is demonstrated. However, laser vibrometry measurements on rotating surfaces are not in general straight forward. Crosstalk between vibration velocity components and harmonic speckle noise generated from the repeating revolution of the surface topography are problems that must be considered. In order to overcome the mentioned issues, a cylindrical casing with a highly optically smooth surface was manufactured and mounted on the tool to be measured. The spindle vibrations, radial tool misalignment, and out-of-roundness of the measured surface were filtered out from the signal; hence, the vibrations of the cutting tool were resolved. Simultaneous measurements of cutting forces and spindle head vibrations were performed and comparisons between the signals were conducted. The results showed that vibration velocities or displacements of the tool can be obtained with high temporal resolution during cutting load and therefore the approach is proven to be feasible for analysing high-frequency milling tool vibrations.     

An analytical force model with shearing and ploughing mechanisms for end milling

An analytical force model with both shearing and ploughing mechanisms is established for the end milling processes. The elemental forces are defined as the linear combination of shearing and ploughing forces in six cutting constants. The analytical model for the total milling forces in the angular and frequency domain are derived by convolution approach and Fourier transform respectively and are expressed as the superposition of the shearing force component and ploughing force component. This dual-mechanism model is analyzed and discussed in the frequency domain and compared with the lumped shear model. An expression is derived for identifying the cutting constants of the dual-mechanism model from the average milling forces. Explicit inclusion of ploughing force in the model is shown to result in better predictive accuracy and yields a linear force model with constant cutting coefficients. Experiments verify the accuracy and the frequency analysis of the dual-mechanism model and show that cutting constants for the dual-mechanism model are fairly independent of chip thickness.     

A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations

The industrial demands for automated machining systems to increase process productivity and quality in milling of aerospace critical safety components requires advanced investigations of the monitoring techniques. This is focussed on the detection and prediction of the occurrence of process malfunctions at both of tool (e.g. wear/chipping of cutting edges) and workpiece surface integrity (e.g. material drags, laps, pluckings) levels. Acoustic emission (AE) has been employed predominantly for tool condition monitoring of continuous machining operations (e.g. turning, drilling), but relatively little attention has been paid to monitor interrupted processes such as milling and especially to detect the occurrence of possible surface anomalies.This paper reports for the first time on the possibility of using AE sensory measures for monitoring both tool and workpiece surface integrity to enable milling of “damage-free” surfaces. The research focussed on identifying advanced monitoring techniques to enable the calculation of comprehensive AE sensory measures that can be applied independently and/or in conjunction with other sensory signals (e.g. force) to respond to the following technical requirements: (i) to identify time domain patterns that are independent from the tool path; (ii) ability to “calibrate” AE sensory measures against the gradual increase of tool wear/force signals; (iii) capability to detect workpiece surface defects (anomalies) as result of high energy transfer to the machined surfaces when abusive milling is applied.Although some drawbacks exist due to the amount of data manipulation, the results show good evidence that the proposed AE sensory measures have a great potential to be used in flexible and easily implementable solutions for monitoring tool and/or workpiece surface anomalies in milling operations.     

Model for surface topography prediction in peripheral milling considering tool vibration

This paper presents a model for the prediction of surface topography in peripheral milling operations taking into account that the tool vibrates during the cutting process. The model includes the effect of tool vibrations in the equations of the cutting edge paths, which are transformed into equivalent polynomial equations and solved for discrete positions along the feed direction by applying a standard root finder. Through this procedure, surface topography generation is simplified with respect to other models in literature. The model allows the topography, the roughness values and the form errors of the milled surface to be predicted. Cutting test results show good agreement with model predictions.     

Complexity measure of motor current signals for tool flute breakage detection in end milling

Automated tool condition monitoring is an important issue in the advanced machining process. Permutation entropy of a time series is a simple, robust and extremely fast complexity measure method for distinguishing the different conditions of a physical system. In this study, the permutation entropy of feed-motor current signals in end milling was applied to detect tool breakage. The detection method is composed of the estimation of permutation entropy and wavelet-based de-noising. To confirm the effectiveness and robustness of the method, typical experiments have been performed from the cutter runout and entry/exit cuts to cutting parameters variation. Results showed that the new method could successfully extract significant signature from the feed-motor current signals to effectively detect tool flute breakage during end milling. Whilst, this detection method was based on current sensors, so it possesses excellent potential for practical and real-time application at a low cost by comparison with the alternative sensors.     

An improved cutting force and surface topography prediction model in end milling

During the milling operation, the cutting forces will induce vibration on the cutting tool, the workpiece, and the fixtures, which will affect the surface integrity of the final part and consequently the product's quality. In this paper, a generic and improved model is introduced to simultaneously predict the conventional cutting forces along with 3D surface topography during side milling operation. The model incorporates the effects of tool runout, tool deflection, system dynamics, flank face wear, and the tool tilting on the surface roughness. An improved technique to calculate the instantaneous chip thickness is also presented. The model predictions on cutting forces and surface roughness and topography agreed well with experimental results.     

Tool life and tool wear in the semi-finish milling of inclined surfaces

Functional die and mold components have complex geometries and are made of high hardness materials, which make them difficult to machine. This work contributes to a better understanding of this type of process and of the wear mechanisms of tools used in semi-finishing operations of hardened steels for dies and molds. Several milling experiments were carried out to cut AISI H13 steel with 50 HR_C of hardness using the high-speed milling technique. The main goal was to verify the influence of workpiece surface inclination and cutting conditions on tool life and tool wear mechanisms. The main conclusions were the inclination of the machined surface strongly influences tool life and tool wear involves different mechanisms. At the beginning of tool life, the wear was caused mainly by abrasion on the flank face plus diffusion and attrition on the rake face. At the end of tool life, the mechanisms were adhesions and microchipping at the cutting edge.     

An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts

Inconel 718 is a difficult-to-cut nickel-based superalloy commonly used in aerospace industry. This paper presents an experimental study of the tool wear propagation and cutting force variations in the end milling of Inconel 718 with coated carbide inserts. The experimental results showed that significant flank wear was the predominant failure mode affecting the tool life. The tool flank wear propagation in the up milling operations was more rapid than that in the down milling operations. The cutting force variation along with the tool wear propagation was also analysed. While the thermal effects could be a significant cause for the peak force variation within a single cutting pass, the tool wear propagation was believed to be responsible for the gradual increase of the mean peak force in successive cutting passes.     

Novel methods for rapid assessment of tool performance in milling

The paper examines the effectiveness of two innovative techniques designed to rapidly optimize a milling application. One of them relates to quantifying the relative wear of different insert grades concurrently in a single cutting test, by mounting the inserts in the same cutter, for a quick comparative performance evaluation. Experimental results that illustrate the validity and limitation of this concept, and a scheme for enhancing the reliability of the test method are presented. The other technique refers to rapid identification of the optimum feed/tooth that corresponds to maximum tool life. This entails a test wherein individual inserts in the cutter are subject to feed/tooth that are multiples of a base value, by selectively leaving appropriate number of consecutive insert pockets unoccupied. These novel techniques complement known accelerated tool life tests, and are expedient for industries that engage short production runs, in terms of selecting a suitable insert grade for an application, and determining optimal cutting conditions for the selected grade.     

The intelligent on-line monitoring of end milling

The main topics discussed in this paper include sensor integration, data extraction, data processing, monitoring the cutting tool, safety of the tool machinery, and quality of the components in processing. The detection method used in this paper is to extract the workload of a spindle motor from a CNC controller, and then transmit the data via a I/O card for further processing. The computer is connected to the CNC by DNC and is able to detect abnormal conditions and transmit, through DNC, to CNC the NC program to stop the machine or to replace the cutting tool. The systematic architectural instrument develops tools with object-oriented professional software and establishes software structure using a visual component library. The software component structure is made easy for maintaining and extending programs and for the operating system with its graphics user interface.     

Influence of machining cycle of horizontal milling on the quality of cutting force measurement for the cutting tool wear monitoring

The cutting tools are today used a lot by industry and they are expensive, so it was interesting to optimize their use, by developing a predictive method of their wear, particularly, the flank wear V _b . For this task, the flank tool wear was measured in off-line using a binocular microscope, whereas, the cutting forces are recorded by means of a dynamometer (Kistler 9255B). The acquired signatures are analyzed during the milling operation throughout the tool life. In this paper, we are interested in the extraction of the appropriate indicators which characterize the tool wear by temporal and frequential analyses of the cutting force signals; and highlighting the influence of the clamp holes and the machining cycle to the quality of the measurements.     

Monitoring of tool fracture in end milling using induction motor current

This paper describes the use of induction motor current to monitor tool fracture in end milling operations. The principles of induction motors are studied in this paper to establish the relationship between the motor current and the motor torque. It is shown that the square of the stator current of induction motors is approximately proportional to the motor torque. Since the occurrence of tool fracture will cause variations in the motor torque, measurement of the stator current appears to be an indirect technique for monitoring tool fracture. A sensitivity analysis of the stator current to the occurrence of tool fracture is also reported. Finally, experimental results under varying cutting conditions have been presented to demonstrate the effectiveness of this approach for the detection of tool fracture in end milling operations.     

Thermal modelling of end milling

Determination of the temperatures during machining is one of the most important challenges for accurate milling simulations. Coupled with excessive shearing, plastic deformation and friction in a small region of cutting, the temperatures in milling may have very significant impact on parts and tools such as dimensional errors, residual stresses and tool wear. Temperature exhibits a non-linear complex-modelling problem in milling process. In this article, for the first time, a novel thermal modelling is introduced for fast and accurate prediction of temperatures in end milling processes. A theoretical modelling approach and experimental validations are presented for various cutting conditions.