A new algorithm for the detection of tool breakage in milling

In this paper a novel signal processing algorithm is presented for the detection of tool breakage using the displacement signal (RORPA). The algorithm extracts information from consecutive revolutions to decrease dependencies on runout conditions and idle noise, creating a much more robust signal for the automatic determination of tool breakage conditions. The algorithm is explained as well as the determination of dynamic thresholds. The thresholds are set using the information regarding the cutting conditions (mean displacement signal and its slope during transient milling). Examples with real data are also presented. A critique of the overall performance and future work incorporating information from x and y channels completes the discussion.     

The shape characteristic detection of tool breakage in milling operations

Detection of tool failure is very important in automated manufacturing. All previously developed tool breakage detection approaches in milling operations have adopted the strategy of parameter detection in which the detection of tool breakage was carried out according to values of specific parameters selected to reflect tool state (with or without tool breakage). In this paper the new concept of shape characteristic detection of tool breakage in milling operations is proposed. The detection of tool breakage is conducted according to the shape characteristics of discrete dyadic wavelet decomposition of cutting force. By means of the proposed method, the influence caused by the variation of cutting parameters and transients is eliminated. The proposed method is conducted in two steps. In the first step, cutting force signals are decomposed by discrete dyadic wavelet, with the shape characteristic vectors then being generated by the proposed shape characteristic vector-generating algorithm. In the second step, the shape characteristic vectors are fast classified by the ART2 neural networks. The accuracy and effectiveness of the proposed method are verified by numerous experiments.     

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.     

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.     

Statistical tool breakage detection schemes based on vibration signals in NC milling

We develop a vibration sensor-based tool breakage detection system for NC milling operations. The system obtains the time-domain vibration signal from the sensor attached on the spindle bracket of our CNC machine and declares tool failures through the on-line monitoring schemes. For the on-line detection, our approach is to use the statistical process control methods where control limits or thresholds are automatically calculated independently of cutting conditions. The main thrust of this paper is to compare the performance of the proposed statistical process monitoring methods including the X-bar control scheme, the exponentially weighted moving average (EWMA) scheme, and the adaptive EWMA scheme. The performance of the control schemes are compared in terms of the type I and II errors calculated from the experiment data.     

End milling tool breakage detection using lifting scheme and Mahalanobis distance

In this paper, a novel method based on lifting scheme and Mahalanobis distance (MD) is proposed for detection of tool breakage via acoustic emission (AE) signals generated in end milling process. The method consists of three stages. First, by investigating the specialty of AE signals, a biorthogonal wavelet with impact property is constructed using lifting scheme, and wavelet transform is carried out to separate AE components from the original signals. Second, Hilbert transform is adopted to demodulate signal envelope on wavelet coefficients and salient features indicating the tool state (i.e., normal conditions, slight breakage, and serious breakage) are extracted. Finally, tool conditions are identified directly through the recognition of these features by means of MD. Practical application results on a CNC vertical milling machine tool show that the proposed method is accurate for feature extraction and efficient for condition monitoring of cutting tools in end milling process.     

Research on tool wear detection based on machine vision in end milling process

This paper suggests a novel technique for the tool wear measurement based on machine vision. Tool images are captured between cutting operations using a machine vision system. The gray value difference threshold is determined from the tool image itself and the reference line is found to locate the tool in the image. The edges of the tool wear region are extracted by column scanning. A method of continuity testing is used to find the correct edge position in each wear column. To achieve a more accurate result, the sub-pixel edge detection technology is adopted to extract the edges. Finaly, the tool wear parameters can be obtained after rebuilding the top edge of the wear region and determining the bottom edge of the wear region. The measurement results gotten by the proposed method are compared with those gotten by measuring directly with a microscope. The proposed scheme is shown to be reliable and effective for the automated tool wear measurement.     

国产化沥青砼路面铣刨刀具的钎焊

研究了沥青砼路面铣刨刀具的钎焊工艺，对铣刨刀具的钎焊接头进行了金相组织的分析和抗剪强度的试验，并在现场通过使用考核。其结果表明，选用国产化的材料，及采用所制定的钎焊工艺，钎接后的沥青砼路面铣刨刀具完全能够满足使用性能的要求。     

Study of milling cutting force pulsation applied to the detection of tool breakage

A study of the cutting force pulsation due to tool breakage is presented. Monitoring algorithms extracting the cutting force signal changes caused by tool breakage and further processing the extracted cutting force signal to recognize tool breakage are proposed. Theoretical studies and experimental results performed in milling operations have proven the feasibility of the algorithms proposed.     

Development of a tool failure detection system using multi-sensors

Tool monitoring and machine diagnosis in real machining have been crucial to the realization of fully automated machining. Also, the on-line detection technique of the tool breakage in machining should be supported. The effect of tool breakage is usually revealed from an abrupt change in the processed measurements, which is in excess of a threshold value. Although these techniques are generally effective for a specific cutting condition, they are often not sufficiently reliable for use in production due to the inability of single measurement to reflect tool breakage under various cutting conditions. In order to enhance the reliability of tool breakage signatures obtained from a single sensor, an integrated approach based on measurements from several sensors has been put forward. In this study, the tool breakage detection method using multi-sensors is proposed and the sensor fusion algorithm is developed to integrate and make decisions from data measured through the multisensors. Also, the performances of this scheme are compared and evaluated with real cutting process.     

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.     

A study of noise emission for tool failure prediction

The relationship between noise emission and tool wear for tungsten carbide P30 and HSS tools and AISI 1045 and AISI 4340 steels are investigated. Results show that a characteristic frequency of around 4–6 kHz exists for a wide range of tool-workpiece materials and cutting conditions. The sound pressure level at the characteristic frequency is distinctly higher than machine and background noise for tungsten carbide tools and shows a good correlation with the flank wear. The noise level falls just before the tertiary wear zone and provides a tremendous potential for tool failure prediction.     

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.     

PLC在精镗机上的应用

可编程控制器(PLC)是以微处理器为核心,综合了计算机技术、通信技术而发展起来的一种新型、通用的自动控制装置,具有结构简单、性能优越、可靠性高、灵活通用、易于编程、使用方便等优点.近年来在工业自动控制、机电一体化、改造传统产业等方面得到了广泛的应用.本文简述了利用日本三菱公司的PLC(FX2N-48MT)控制器对精镗机...     

Lightweight semi-actively damped high performance milling tool

Long cantilevered high performance milling tools tend to vibrate during machining operation due to process excitation. This impairs the quality of the workpiece surface and limits the achievable material removal rate. An optimisation of the dynamic properties of these tools enables an increased machining performance. This paper introduces a lightweight design of a shell end milling tool with an integrated semi-active damping system based on magnetorheological fluids. The investigations show that this approach allows an adjustment of the dynamic behaviour of the tool. In machining experiments a significant increase of the material removal rates and improved surface quality are achieved.     

High-speed five-axis milling of hardened tool steel

This paper investigates critical issues related to high-speed five-axis milling of hardened D2 tool steel (hardness HRc 63). A forging die cavity was designed to represent the typical features in dies and molds and to simulate several effects resulting from complex tool path generation. Cutting tool materials used were coated carbide for the roughing and semi-finishing processes and polycrystalline cubic boron nitride (PCBN) for the finishing process. The effects of complex tool paths on several critical machining issues such as chip morphology, cutting forces, tool wear mechanisms, tool life and surface integrity were also investigated. The main tool failure mode was chipping due to the machine tool dynamics. A five-axis analytical force model that includes the cutter location (CL) data file for computing the chip load has been developed. The effect of instantaneous tilt angle variation on the forces was also included. Verification of the force model has been performed and adopted as a basis for explaining the difficulties involved with high-speed five-axis milling of D2 tool steel.     

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.     

CAD assisted chatter-free NC tool path generation in milling

A chatter vibration-free NC tool path preparation strategy is presented for CAD/CAM systems. The stability charts, which contain chatter-free axial depth and radial width of cuts at a practical cutting speed range, are identified from time domain simulation of dynamic end milling operations. The time domain simulation system allows multiple modes of the machine tool-end mill structure whose transfer functions are measured. The existing pocketing routines in a CAD/CAM system are corrected by planning chatter-free axial depth and radial width of cuts that are automatically selected from the stability data bank during NC tool path generation. A newly introduced linear tool-path pocketing strategy is shown to be the most productive algorithm for dynamically corrected tool paths. The method is supported by experimental results.