刀具磨损监测与车削仿真研究

金属加工过程中,切削刀具的状态对于生产效率和表面加工质量有重要影响,因此刀具磨损监测具有重要意义。刀具磨损监测是柔性制造系统研究工程的一个重要课题。切削力信号作为加工过程中最稳定和最可靠的信号,和刀具磨损密切相关。从实验上分析切削力与刀具磨损的相关性,提出刀具切削力变化与磨损变化是一致的。基于有限元分析软件对车削加工进行仿真研究,模拟了切削力的大小分布,并将模拟结果与实验结果进行了比较分析,为实际工艺参数的选择提供了理论指导。     

基于声发射技术监测刀具磨损的研究

刀具磨损的研究方法很多,本文针对近些年发展的声发射技术(AE)在监测刀具磨损上的应用,采用理论分析和现场试验的方法进行可行性分析和验证,结果表明:在考察的几个影响声发射信号强度的因素之中,刀具主切削刃后刀面磨损量对其影响最为显著,这为利用AE技术研究刀具磨损提供了可行性依据;通过对铣刀AE信号进行时域振铃分析,清晰再现了刀具在不同时刻的磨损情况。     

金属车削刀具磨损研究现状

新材料和难加工材料的发展对金属加工制造提出更高的要求,如何在加工时更环保有效地使用刀具、保持加工精度和减小误差成为当前亟代解决的问题。本文论述了影响刀具磨损的因素、不同磨损机理以及刀具磨损模型的研究,总结了切削加工时影响切削界面温度、切削力的因素以及测量方法,并对刀具磨损的研究现状进行了归纳,指出了刀具磨损研究的发展趋势。     

分析数控车削中刀具磨损对加工精度的具体影响情况

20世纪80年代以来，因为数控技术提升，让机床的主轴和都有了很大幅度的提升，随着现今制造技术的全面升级和推动下，i进系统一些功能的零件制造有了新的突破，这让主轴的运转速度和给进速度数控车削技术也进入了新的开发领域。刀具的磨损致使部件的尺寸出现误差，而误差是大是小就是由刀具的磨损程度以及刀具的切削刃和部件的构造形状决定的，因为切削的刀具受到的磨损是持续不断的，所以为了保障部件的加工精度，当切削的刀具出现磨损而且达到了一定的磨损程度或是磨损量超出了允许的范围，这都要及时的进行维修或是更换进行重新的调整。     

基于模糊聚类的数控车削加工刀具磨损检测研究

将模糊聚类分析原理应用于数控车削加工刀具磨损检测,对数控车削加工刀具磨损的各阶段力信号和振动信号进行采集,通过小波滤波及功率谱的谱分析,找到车削加工过程中刀具磨损的典型参数变化。通过提取信号特征值进行模糊聚类,实现了数控车削加工刀具磨损的状态识别。     

陶瓷刀具车削TC4钛合金磨损特性的研究

为研究陶瓷刀具切削钛合金的磨损机理,采用CC6060陶瓷刀片对TC4钛合金进行了干式车削试验。结果表明:陶瓷刀具干式切削TC4钛合金时,磨损形貌以前刀面月牙洼磨损、后刀面沟槽磨损和刀尖破损为主,磨损机理主要是粘结磨损和氧化磨损。随着切削速度的增加,刀具磨损加剧,刀具寿命降低。CC6060陶瓷刀片干式切削钛合金时的使用寿命很低,不适于干式切削钛合金。     

干式冷风车削不锈钢的高速钢刀具磨损试验研究

采用低温冷风射流技术,对高速钢刀具加工不锈钢工件的磨损情况进行了试验研究。加工时采用低温冷风射流空气代替传统的切削液,不仅起到有效的冷却和润滑作用,而且能够避免环境污染。通过干式常温切削和干式冷风切削1Cr18Ni9Ti的不锈钢的对比实验,探讨了干式低温冷风切削对高速钢刀具寿命的影响,并且发现了在冷风切削作用下积屑瘤生成的新特点。     

刀具磨损识别的人工神经网络方法

人工神经网络在机械制造过程监控中有着广阔的应用前景。本文提出了一种基于多层前馈网络和误差反向传播（ＢＰ）算法的刀具磨损识别的新方法，并研究了网络结构和网络输入参数的选择，以建立较完善的神经网络模型。初步验证结果表明：基于多种传感器信息和工艺参数的人工神经网格模型对刀具状态具有很好的识别能力。     

纯钒车削刀具磨损表面形态及机理研究

对金刚石刀具、涂层刀具及硬质合金刀具车削纯钒时刀具磨损形态及其磨损机理进行观察和分析.结果表明,在所选取条件下,不同刀具材料对工件材料切削时表现出的刀具磨损形态主要为前刀面磨损、后刀面磨损、微崩刃、剥落和粘结等.刀具的前刀面主要是沿切屑流出方向的沟槽形月牙洼磨损,而后刀面以粘结磨损为主.CD10刀具和H10非涂层刀具具有较佳的切削性能,而H13A非涂层刀具和GC1025涂层刀具不适于纯钒车削.     

An artificial-neural-networks-based in-process tool wear prediction system in milling operations

An artificial-neural-networks-based in-process tool wear prediction (ANN-ITWP) system has been proposed and evaluated in this study. A total of 100 experimental data have been received for training through a back-propagation ANN model. The input variables for the proposed ANN-ITWP system were feed rate, depth of cut from the cutting parameters, and the average peak force in the y-direction collected online using a dynamometer. After the proposed ANN-ITWP system had been established, nine experimental testing cuts were conducted to evaluate the performance of the system. From the test results, it was evident that the system could predict the tool wear online with an average error of ±0.037 mm. Experiments have shown that the ANN-ITWP system is able to detect tool wear in 3-insert milling operations online, approaching a real-time basis .     

数控机床刀具磨损监测实验数据处理方法研究

数控机床刀具磨损监测对于提高数控机床利用率,减小由于刀具破损而造成的经济损失具有重要意义.有针对性地回顾了国内外各种分析刀具磨损信号方法的研究工作,详细叙述了功率谱分析法、小波变换、人工神经网络以及多传感器信息融合技术的实现形式.通过比较各种数据处理方法的优缺点,提出基于混合智能多传感器信息融合技术是数控机床刀具磨损监测实验数据处理的未来发展的主要方向.     

数控机床刀具磨损监测方法研究

数控机床刀具磨损监测对于提高数控机床利用率,减小由于刀具破损而造成的经济损失具有重要意义.文章有针对性地回顾了国内外各种刀具磨损监测方法的研究工作,详细叙述了切削力监测法、切削噪声监测法、功率监测法、声发射监测法、电流监测法以及基于多传感器监测法等六种刀具磨损监测方法.本文通过比较各种监测方法的优缺点,提出基于多传感器监测法是数控机床刀具磨损监测方法的未来发展的主要方向.     

2D FEM estimate of tool wear in turning operation

Finite element method (FEM) is a powerful tool to predict cutting process variables, which are difficult to obtain with experimental methods. In this paper, modelling techniques on continuous chip formation by using the commercial FEM code ABAQUS are discussed. A combination of three chip formation analysis steps including initial chip formation, chip growth and steady-state chip formation, is used to simulate the continuous chip formation process. Steady chip shape, cutting force, and heat flux at tool/chip and tool/work interface are obtained. Further, after introducing a heat transfer analysis, temperature distribution in the cutting insert at steady state is obtained. In this way, cutting process variables e.g. contact pressure (normal stress) at tool/chip and tool/work interface, relative sliding velocity and cutting temperature distribution at steady state are predicted. Many researches show that tool wear rate is dependent on these cutting process variables and their relationship is described by some wear rate models. Through implementing a Python-based tool wear estimate program, which launches chip formation analysis, reads predicted cutting process variables, calculates tool wear based on wear rate model and then updates tool geometry, tool wear progress in turning operation is estimated. In addition, the predicted crater wear and flank wear are verified with experimental results.     

Modeling of tool wear during hard turning with self-propelled rotary tools

In this paper, an attempt is made to evaluate the self-propelled rotary carbide tool performance during machining hardened steel. Although several models were developed and used to evaluate the tool wear in conventional tools, there were no attempts in open literature for modeling the progress of tool wear when using the self-propelled rotary tools. Flank wear model for self-propelled rotary cutting tools is developed based on the work-tool geometric interaction and the empirical function. A set of cutting tests were carried out on the AISI 4340 steel with hardness of 54–56 HRC under different cutting speeds and feeds. The progress of tool wear was recorded under different interval of time. A genetic algorithm was developed to identify the constants in the proposed model. The comparison of measured and predicted flank wear showed that the developed model is capable of predicting the rate of rotary tool flank wear progression.     

Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals

Micro scale machining process monitoring is one of the key issues in highly precision manufacturing. Monitoring of machining operation not only reduces the need of expert operators but also reduces the chances of unexpected tool breakage which may damage the work piece. In the present study, the tool wear of the micro drill and thrust force have been studied during the peck drilling operation of AISI P20 tool steel workpiece. Variations of tool wear with drilled hole number at different cutting conditions were investigated. Similarly, the variations of thrust force during different steps of peck drilling were investigated with the increasing number of holes at different feed and cutting speed values. Artificial neural network (ANN) model was developed to fuse thrust force, cutting speed, spindle speed and feed parameters to predict the drilled hole number. It has been shown that the error of hole number prediction using a neural network model is less than that using a regression model. The prediction of drilled hole number for new test data using ANN model is also in good agreement to experimentally obtained drilled hole number.     

A method for predicting hobbing tool wear based on CNC real-time monitoring data and deep learning

Intelligent monitoring and diagnosis of tool status are of great significance for improving the manufacturing efficiency and accuracy of the workpiece. It is difficult to quickly and accurately predict the wear state of worm gear hob under different working conditions. This paper proposes a novel approach to predict hob wear status based on CNC real-time monitoring data. Based on the open platform communication unified architecture (OPC UA) technology and orthogonal test, the machine data of motor power, current, etc. related to tool wear are collected online in the worm gear machining process. And then, an improved deep belief network (DBN) is used to generate a tool wear model by training data. A growing DBN with transfer learning is introduced to automatically decide its best model structure, which can accelerate its learning process, improve training efficiency and model performance. The experiment results show that the proposed method can effectively predict hob wear status under multi-cutting conditions. To show the advantages of the proposed approach, the performance of the DBN is compared with the traditional back propagation neural network (BP) method in terms of the mean-squared error (MSE). The compared results show that this tool wear prediction method has better prediction accuracy than the traditional BP method during worm gear hobbing.     

Prediction of tool wear using regression and ANN models in end-milling operation

Tool wear prediction plays an important role in industry for higher productivity and product quality. Flank wear of cutting tools is often selected as the tool life criterion as it determines the diametric accuracy of machining, its stability and reliability. This paper focuses on two different models, namely, regression mathematical and artificial neural network (ANN) models for predicting tool wear. In the present work, flank wear is taken as the response (output) variable measured during milling, while cutting speed, feed and depth of cut are taken as input parameters. The Design of Experiments (DOE) technique is developed for three factors at five levels to conduct experiments. Experiments have been conducted for measuring tool wear based on the DOE technique in a universal milling machine on AISI 1020 steel using a carbide cutter. The experimental values are used in Six Sigma software for finding the coefficients to develop the regression model. The experimentally measured values are also used to train the feed forward back propagation artificial neural network (ANN) for prediction of tool wear. Predicted values of response by both models, i.e. regression and ANN are compared with the experimental values. The predictive neural network model was found to be capable of better predictions of tool flank wear within the trained range.     

An investigation of tool wear in high-speed turning of AISI 4340 steel

A commercially available insert has been used to turn an AISI 4340 steel at speeds placed between 325 and 1000 m/min. The flank wear was measured in connection to cutting time. This is to determine the tool life defined as the usable time that has elapsed before the flank wear has reached the criterion value.It is shown that an increase in cutting speed causes a higher decrease of the time of the second gradual stage of the wear process. This is due to the thin coat layer which is rapidly peeled off when high-speed turning.The investigation included the realization of a wear model in relation to time and to cutting speed. An empirical model has also been developed for tool life determination in connection with cutting speed.On the basis of the results obtained it is possible to set optimal cutting speed to achieve the maximum tool life.