Cutting tool fracture prediction and strength evaluation by stress identification, part i: Stress model

Cutting tool premature failure, caused by tool fracture and chipping, is a frequent problem in the metal cutting area. For a certain type of cutting tool, correctly identifying its load profile and property profile is very crucial for prediction of the tool premature failure. The most direct way to evaluate the load profile and property profile of a cutting tool is to identify the stress working on it. This paper presents a new method for identification of the maximum principal stress and maximum effective stress subjected to a cutting tool in a cutting process. The method consists of four steps: estimation of the contact load on the tool faces, calculation of the maximum related stresses with the FEM, modelling of maximum related stresses with an artificial neural network and, finally, identification of the maximum principal stress, σ_e, and maximum effective stress, σ_e, with the use of measured cutting forces or cutting parameters. The calculation of the contact load on the tool face is based on a simplification of the load distribution on the tool face. Part I of the paper will present this method and Part II will present the results of experimental studies.     

Tool life testing in gundrilling: an application of the group method of data handling (GMDH)

Although the known techniques of the design of experiments (DOE) allows significant improvements in the methodology of machining tests, their use in metal cutting is restricted by limited number of variables included into consideration, pre-set model, subjective pre-process decisions and uncertainties in the machining data collected experimentally. This paper introduces an application of a new powerful technique of DOE known as the group method of data handling (GMDH) to the design of tool life experiments that allows avoiding the above-mentioned disadvantages of traditional DOE. As a result of such an application, a mathematical model that correlates tool life in gundrilling of cast iron with various regime and design parameters was obtained. Special care was taken to avoid the influence of the dynamic phenomenon of the gundrilling process on the obtained experimental results. The obtained mathematical model reveals that tool life in gundrilling is a complex function of various regime and design parameters.     

基于DE—BP神经网络的刀具寿命预测研究

引入了BP神经网络技术对刀具寿命进行预测,建立了刀具寿命预测模型.并针对BP神经网络所存在的缺陷,结合差异演化算法,提出了实数编码的DE-BP神经网络预测模型.实验表明,该模型对刀具寿命预测精度高,为刀具需求计划制定、成本核算、切削参数制定提供了理论依据,节约了制造执行系统成本.     

A novel analytical algorithm for prediction of workpiece temperature in end milling

Temperature is a critical parameter in machining as it directly affects the cutting performance, part quality, residual stresses, distortion, tool life, etc. In this article, a novel analytical algorithm for fast temperature prediction in intermittent cutting processes like milling is proposed. For the first time, the temperature drop during the noncutting period is taken into consideration for the workpiece side. The model also takes into account time-varying chip thickness due to the trochoidal motion of the milling tool. Validation tests with Ti6Al4V showed the promise of the algorithm in predicting the milling temperature under various cutting conditions.     

基于单温度传感器的数控机床主轴热误差建模方法研究

为了提高热误差模型的预测精度和减少布置在机床内部的温度传感器数量,提出一种基于单个温度传感器数据的主轴轴向热误差辨识模型。该模型的输入由单个温度传感器采集的数据处理生成,内部参数少,利用智能优化算法的全局寻优能力辨识模型参数,减少人工干预,使得模型泛化性更强。以某型号三轴机床为实验对象,通过机床切削工件,验证模型辨识效果。通过与神经网络主轴热误差预测模型对比分析及实验验证,结果表明：提出的热误差模型预测主轴轴向热误差的残差较小,预测精度较高,且具有内部参数少和泛化能力强等优点,可支持数控机床的集成应用。     

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.     

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.     

基于GMDH与SVM的信号缺失情景下轴承故障诊断

针对在轴承故障数据采集过程中,由于外界干扰导致部分信号缺失的问题,提出一种基于数据分组处理算法(GMDH)与经粒子群优化的支持向量机(PSO-SVM)算法相结合的轴承故障诊断方法。首先,利用GMDH算法搭建时间序列预测模型,预测出并弥补上缺失的数据并与采集信号整合;其次,经过PSO-SVM模型对完整的数据集进行故障类型诊断;最后,实验采用了凯斯西储大学轴承故障振动数据,并与SVM、PSO-SVM等算法进行比较,验证了在信号缺失情景下,GMDH-SVM混合诊断方法的有效性。     

Slicing Process Monitoring of Quartz Glass Ferrules using Acoustic Emission

Inappropriate conditions and blade wear in the glass ferrule slicing process are likely to cause chipping, scratching, and inaccuracies in ferrules. To limit such abnormal occurrences, in-process monitoring of the slicing process is necessary. The AE (Acoustic Emission) signal is known to be a useful tool to detect cracks and chippings in cutting brittle materials such as ceramics and glass, because high-frequency acoustic waves are emitted in material fracture. In this study, the glass ferrule slicing process was analyzed and modeled in two stages - cutting and polishing - according to the relative position of the blade and the glass rod. The AErms (root mean square of AE) for various slicing cases, was acquired and investigated both qualitatively and quantitatively based on the process model, the AE feature was extracted for each abnormal state: the high-frequency component of AE for chipping and the low-frequency component for wear. In this paper, a monitoring algorithm using two index parameters - kurtosis and level ratio - of AErms is proposed to discriminate abnormal states - chipping and wear - in the glass ferrule slicing operation.     

Development of a reference cutting force model for rough milling feedrate scheduling using FEM analysis

Recently developed feedrate scheduling systems regulate cutting forces at the desired level by changing the feedrate to reduce the machining time and to avoid undesirable situations. For effective scheduling, an optimized criterion is required to adjust the feedrate. In this study, a method to obtain the most appropriate reference cutting force for rough milling was developed. The reference cutting force was determined by considering the transverse rupture strength of the tool material and the area of the rupture surface. A finite element method analysis was performed to accurately calculate the area of the rupture surface. Using the analyzed results, the effect of various cutting parameters on the chipping phenomenon was determined. The calculation method for the reference cutting force considered the area of the rupture surface, the effect of the rake angle, and the axial depth of the cut. The reference cutting force calculated using the developed model was applied to feedrate scheduling for pocket machining. The experimental results clearly show that the reference cutting force obtained from the proposed method met the desired constraints that guarantee higher productivity without tool failure.     

变双曲圆弧齿线齿轮切削仿真及参数优化

变双曲圆弧齿线圆柱齿轮（VH-CATT）是一种将圆弧齿线运用于齿轮齿线上的新型齿轮。由于缺少专用的加工机床,导致加工时其切削参数的调整较为复杂。为解决这个问题,根据其啮合原理及加工过程,利用ABAQUS对其切削加工过程进行模拟仿真;利用得到的数据,根据正交试验方法建立切削力的预测模型。利用鲸鱼算法,建立以加工效率与较小切削力为目标的函数优化模型,并通过加权求和法与归一化处理将它转化为单目标函数优化模型,通过鲸鱼算法得到优化后的切削参数。结果表明：所提出的单目标函数优化模型能够很好地对切削参数进行选定优化,以得到更好的加工效果;优化后的切削参数为主轴转速n=189.3 r/min,每齿进给量fz=0.046 mm,切削深度ap=1.89 mm。     

陶瓷刀具断续车削淬硬钢失效形态研究

采用Al_2O_3/Ti(C,N)陶瓷刀具进行淬硬钢的断续车削正交试验,对不同切削速度下刀具的失效形态进行了对比。结果表明,低速时,刀具的失效形态主要是崩刃和前刀面剥落,疲劳破损影响较小。随着切削速度的增加,疲劳破损对刀具的影响逐渐增大。高速时,疲劳裂纹扩展引起的破损成为刀具主要失效形式。在不同切削速度下,刀具内部的应力水平不同,导致裂纹扩展速率及裂纹方向有所差异,疲劳特征则表现出不同形式。低速时疲劳特征表现为疲劳条带,而高速时的疲劳特征通常为疲劳弧线。     

Real-time determination of cutting force coefficients without cutting geometry restriction

The determination of the cutting force coefficients is a critical point in the case of using the mechanistic cutting force model for predicting the forces during milling processes. The main reason is that the computations require a series of experiments with special geometrical conditions, and the validity of the results is limited. In this paper a cutting force predicting method, based on the mechanistic cutting force model will be introduced, together with an algorithm for determining the cutting force coefficients in the course of a single experiment without restrictions in regard to the cutting geometry. Besides the fact that the proposed method lifts the geometrical restrictions of the previously published solutions, it makes it possible to calculate the coefficients just when they are needed for force prediction right at the machining process, to avoid the problem of the limited validity of the coefficients. In this case the real-time measuring of the cutting forces is needed, while the forthcoming forces can be predicted with an appropriate look-forward algorithm, which is also presented.     

Increasing productivity in sculpture surface machining via off-line piecewise variable feedrate scheduling based on the force system model

Sculpture surface machining is a critical process commonly used in various industries such as the automobile, aerospace, die/mold industries. Since there is a lack of scientific tools in practical process planning stages, feedrates for CNC machining are selected based on the trial errors and previous experiences. In the selections of the process parameters, production-planning engineers are conservative in order to avoid undesirable results such as chipping, cutter breakage or over-cut due to excessive cutter deflection. Currently, commonly used CAD/CAM programs use only the geometric and volumetric analysis, but not the physics of the processes, and rely on experience based cutting tool database and users’ inputs for selection of the process parameters such as feed and speed. Usually, the feeds and cutting speeds are set individual constant values all along the roughing, semi-finishing, and finishing processes. Being too conservative and setting feedrate constant all along the tool path in machining of sculpture surfaces can be quite costly for the manufacturers. However, a force model based on the physics of the cutting process will be greatly beneficial for varying the feedrate piecewise along the tool path.The model presented here is the first stage in order to integrate the physics of the ball-end milling process into the selection of the feeds during the sculpture surface machining. Therefore, in this paper, an enhanced mathematical model is presented for the prediction of cutting force system in ball end milling of sculpture surfaces. This physical force model is used for selecting varying and ‘appropriate’ feed values along the tool path in order to decrease the cycle time in sculpture surface machining. The model is tested under various machining conditions, and some of the results are also presented in the paper.     

The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless steel

The purpose of this paper is to develop a predictive model for the prediction of tool flank wear and an optimization model for the determination of optimum cutting conditions in machining 17-4PH stainless steel. The back-propagation neural network (BPN) was used to construct the predictive model. The genetic algorithm (GA) was used in the optimization model. The Taguchi method (TM) was used to find the optimum parameters for both models, respectively. Two steps of experiments have been carried out by machining 6 mm length and 90 mm length of the workpiece, respectively. The experimental scheme was arranged by using an orthogonal array of TM. It has been shown that the predictive model is capable of predicting the tool flank wear in an agreement behavior. The optimization model has also been proved that it is a convenient and efficient method to find the optimum cutting conditions associated with the maximum metal removal rate (MMRR) under different constraints. The constraint is the tool flank wear that can be determined from the predictive model. Furthermore, the systematic procedure to develop the models in this paper can be applied to the usage of the predictive or optimized problems in metal cutting.     

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.     

基于改进LSTM-AdaBoost的铣刀磨损量预测

针对铣刀磨损量预测时精度低的问题,提出一种基于黑寡妇算法（BWO）优化的长短期记忆神经网络（LSTM）与AdaBoost集成学习算法相结合的铣刀磨损量预测方法。在铣刀磨损振动信号中提取时域、频域以及时频域多域特征。通过BWO算法优化LSTM的核心参数,并将优化后的LSTM网络与AdaBoost算法进行结合,构建铣刀磨损量预测模型。最后用PHM Society 2010铣刀全寿命周期的振动数据进行实验。研究结果表明：所提方法能够有效地预测出铣刀磨损量变化值,优化后模型的平均绝对误差百分比为3.436%、均方根误差为6.471、决定系数 R2 为0.935。该方法能够获得准确率更高的铣刀磨损量预测值,预测效率更高。     

Deviation of a machined surface in flank milling

The flatness defects observed in flank milling with cutters of long series are mainly due to the tool deflections during the machining process. This article present the results of an identification procedure of the coefficients of a force model for a given tool workpiece couple for the prediction of the defects of the tool during the cutting. The calibration method proposed meets a double aim: to define an experimental protocol that takes the industrial constraints of time and cost into account and to work out a protocol which minimizes uncertainties likely to alter the interpretation of the results (environmental, software or mechanical uncertainties). For that, the procedure envisages the machining of a simple plane starting from a raw part formed by a tilted plane, allowing for the variation of the tool engagement conditions. The tool deviation during the cutting process is indirectly identified by measuring the machined surface. The observed straightness defect conditions can be explained by the evolution of the cutting pressures applied to the cutting edges in catch during the cutter rotation. The precision was considerably improved by the taking into account of the cutter slope defect in the calculation of the load applied to the tool. After identification of the tool-workpiece couple, the prediction model was applied to some examples and allowed to determine the variations of form and position of the surface points with a margin of 5%.     

An expert troubleshooting system for the milling process

Common problems experienced in milling processes include forced and chatter vibrations, tolerance violations, chipping and premature wear of the tools. This paper presents an expert system which attempts to troubleshoot the source of milling problems by utilising dynamics data coupled with the opinion of the operator and acoustic Fourier spectrum data taken from the cutting process. The expert system utilises a fuzzy logic based process to interpret the signals and information, and recommends possible alterations to the process to achieve high-performance milling operations.Specific inference engines were developed to assess the chatter stability, variation in cutting force coefficient, tool run-out and forced vibration characteristics of the system. Lastly, a stability lobe plot interpretation engine to automate the lobe selection process and recommend new, chatter free cutting conditions, was also developed. The chatter stability inference engine was tested with real cutting data, through acoustic measurements taken from various cutting conditions on an aluminium milling process. The chatter inference engine successfully determined the stability of the system for each sampled cutting condition. The robustness of the troubleshooting system depends on the accuracy of acoustic and frequency response measurements.     

A multi-sensor strategy for tool failure detection in milling

A multi-sensor monitoring strategy for detecting tool failure during the milling process is presented. In this strategy, both cutting forces and acoustic emission signals are used to monitor the tool condition. A feature extracting algorithm is developed based on a first order auto-regressive (AR) model for the cutting force signals. This AR(1) model is obtained by using average tooth period and revolution difference methods. Acoustic emission (AE) monitoring indices are developed and used in determining the setting threshold level on-line. This approach was beneficial in minimizing false alarms due to tool runout, cutting transients and variations of cutting conditions. The proposed monitoring system has been verified experimentally by end milling Inconel 718 with whisker reinforced ceramic tools at spindle speeds up to 3000 rpm.