Tool condition monitoring in milling based on cutting forces by a neural network

Automated machining systems require reliable online monitoring processes. The application of a multilayered neural network for tool condition monitoring in face milling is introduced and evaluated against cutting force data. The work uses the back-propagation algorithm for training neural network of 5 2 10 2 2 architecture. An artificial neural network was used for feature selection in order to estimate flank wear ( Vb ) and surface roughness ( Ra ) during the milling operation. The relationship of cutting parameters with Vb and Ra was established. The sensor selection using statistical methods based on the experimental data helps in determining the average effect of each factor on the performance of the neural network model. This model, including cutting speed, feed rate, depth of cut and two cutting force components (feed force and vertical Z -axis force), presents a close estimation of Vb and Ra . Therefore, the neural network with parallel computation ability provides a possibility for setting up intelligent sensor systems.     

An effective sensor for tool wear monitoring in face milling: Acoustic emission

Acoustic Emission (AE) has been widely used for monitoring manufacturing processes particularly those involving metal cutting. Monitoring the condition of the cutting tool in the machining process is very important since tool condition will affect the part size, quality and an unexpected tool failure may damage the tool, work-piece and sometimes the machine tool itself. AE can be effectively used for tool condition monitoring applications because the emissions from process changes like tool wear, chip formation i.e. plastic deformation, etc. can be directly related to the mechanics of the process. Also AE can very effectively respond to changes like tool fracture, tool chipping, etc. when compared to cutting force and since the frequency range is much higher than that of machine vibrations and environmental noises, a relatively uncontaminated signal can be obtained. AE signal analysis was applied for sensing tool wear in face milling operations. Cutting tests were carried out on a vertical milling machine. Tests were carried out for a given cutting condition, using single insert, two inserts (adjacent and opposite) and three inserts in the cutter. AE signal parameters like ring down count and rms voltage were measured and were correlated with flank wear values (VB max). The results of this investigation indicate that AE can be effectively used for monitoring tool wear in face milling operations.     

Investigation on tool wear process of milling wave-transmitting Si3N4 ceramics

Due to the low fracture toughness of wave-transmitting Si₃N₄ ceramics, the special material removal mechanism causes the tool wear to be different. The paper presents the tool wear forms and mechanism under different milling depth. The effect of tool wear on cutting force and machined surface morphology is discussed. Tests have been performed under typical conditions of cutting depth of 0.3 mm (in plastic-domain processing) and 0.4 mm (in brittle-domain processing). The results show that the abrasive wear caused by the chips is the main mechanism of the cutting edge wear and the flank wear, the increase of the side edge rear angle with tool wear is the main cause of the chipping phenomenon. The cutting depth is a significant influence parameter to the wear characteristics, and two types have been distinguished. As the material removal volume ascending, the cutting edge wear and the flank face wear has a stable period, and the root-mean-square deviation of processing surface increases to 1.6 μm, while that increase with the material removal volume continuously, and the processing surface decreases to 1.4 μm. It has been proved that the cutting force tends to increase first and then decrease as the material removal volume is about 4320 mm³.     

Experimental Study of Surface Roughness and Tool Flank Wear during Hybrid Milling

Two advanced machining methods such as thermally enhanced machining and ultrasonic-assisted machining are recently considered in many studies. In this article, a new hybrid milling process is presented by gathering the characteristics of these two methods. In order to determine the axial depth of cut and engagement in the process, three-dimensional thermal finite-element analysis is applied to determine the dimensions of softened materials. Finite-element modal analysis is used to determine the dimensions and clamping state of the workpiece while cutting area has the highest vibration amplitude. Full factorial experimental design is applied to investigate the effect of hybrid machining parameters on the surface roughness and tool wear. Tool flank wear was investigated under the condition of constant cutting speed during different period of times. Hybrid milling process with an amplitude of 6 µm and a temperature of 900°C creates a surface with 42% lower roughness in comparison to conventional milling in feed 0.08 mm/tooth. In a study of tool flank wear, the results show that application of TEUAM decreases flank wear at least 16% in comparison to all other processes.     

Investigation on tool wear and tool life prediction in micro-milling of Ti-6Al-4V

Short tool life and rapid tool wear in micromachining of hard-to-machine materials remain a barrier to the process being economically viable. In this study, standard procedures and conditions set by the ISO for tool life testing in milling were used to analyze the wear of tungsten carbide micro-end-milling tools through slot milling conducted on titanium alloy Ti-6 Al-4 V. Tool wear was characterized by flank wear rate,cutting-edge radius change, and tool volumetric change. The effect of machining parameters, such as cutting speed and feedrate, on tool wear was investigated with reference to surface roughness and geometric accuracy of the finished workpiece. Experimental data indicate different modes of tool wear throughout machining, where nonuniform flank wear and abrasive wear are the dominant wear modes. High cutting speed and low feedrate can reduce the tool wear rate and improve the tool life during micromachining.However, the low feedrate enhances the plowing effect on the cutting zone, resulting in reduced surface quality and leading to burr formation and premature tool failure. This study concludes with a proposal of tool rejection criteria for micro-milling of Ti-6 Al-4 V.     

Acoustic emission analysis for tool wear monitoring in face milling

Monitoring the condition of the cutting tool in any machining operation is very important since it will affect the workpiece quality and an unexpected tool failure may damage the tool, workpiece and sometimes the machine tool itself. Advanced manufacturing demands an optimal machining process. Many problems that affect optimization are related to the diminished machine performance caused by worn out tools. One of the most promising tool monitoring techniques is based on the analysis of Acoustic Emission (AE) signals. The generation of the AE signals directly in the cutting zone makes them very sensitive to changes in the cutting process. Various approaches have been taken to monitor progressive tool wear, tool breakage, failure and chip segmentation while supervising these AE signals. In this paper, AE analysis is applied for tool wear monitoring in face milling operations. Experiments have been conducted on En-8 steel using uncoated carbide inserts in the cutter. The studies have been carried out with one, two and three inserts in the cutter under given cutting conditions. The AE signal analysis was carried out by considering signal parameters such as ring down count and RMS voltage. The results show that AE can be effectively used to monitor tool wear in face milling operation.     

On-line tool condition monitoring in face milling using current and power signals

The vast majority of tool condition monitoring systems use the cutting force as the predictor signal. However, due to prohibitive cost to performance ratios and maintenance and operational problems, such methods are not favoured by industries. In this paper, a method for continuous on-line estimation of tool wear, based on the inexpensive spindle motor current and voltage measurements, is proposed for the complex and intermittent cutting face milling operation. Sensors for these signals are free from problems associated with the cutting forces and the vibration signals. Novel signal processing strategies have been proposed for on-line computation of useful features from the measured signals. Feature space filtering is introduced to obtain robust and improved predictors from the extracted features. A multiple linear regression model, built on the filtered features, is then used to estimate tool wear in real-time. Very accurate predictions are achieved for both laboratory and industrial experiments, surpassing earlier results using cutting forces and estimation methods based on complex methodologies such as artificial neural networks.     

双顶角钻头钻削CFRP复合材料的刀具磨损机制

为了研究碳纤维增强树脂基(CFRP)复合材料切削中刀具在不同部位的磨损机制和规律,以典型的硬质合金双顶角钻头作为研究对象,主要研究对出口分层影响较大的横刃和对最终制孔成型影响较大的第二主切削刃的磨损机制及规律。通过减小磨损测量间隔,并引入切削刃钝圆半径以及后刀面磨损带宽度,表征了横刃和第二主切削刃在加工中的衰变过程。基于显微刃口观测和钝圆半径变化,揭示横刃易崩刃和第二主切削刃磨损后又受到重新刃磨的磨损机制,获得了此类型钻头不同部位的磨损规律。同时,基于上述的磨损表征,研究不同切削部位磨损量对钻削轴向力和力矩的影响,横刃轴向力与横刃钝圆半径变化相关性较小,而钻削最大力矩与第二主切削刃后刀面磨损变化规律相一致。     

Real-time tool wear condition monitoring in turning

A new method to monitor tool wear condition in real time using feed-motor current measured with the aid of inexpensive current sensors installed on the AC servomotor of a CNC turning centre is presented. To achieve this, the feed drive system model is analysed, the feed-motor current is measured, and the relations between feed-motor current, cutting force, and tool flank wear are addressed. The functional dependence of the feed-motor current on tool wear is then expressed in the form of a difference equation relating variation in the feedmotor current to tool flank wear rate. The computerized system automatically compares successive feed-motor current and determines the onset of accelerated tool wear in order to issue a request for tool replacement. Experimental results show that this method of tool wear condition monitoring is effective and industrially applicable.     

Prospects for in-process diagnosis of metal cutting by monitoring vibration signals

Vibration signals from various metal cutting processes in e frequency range of a few Hz to several MHz have been investigated by many researchers for their possible application to an in-process cutting condition monitoring system and some remarkable laboratory results have been reported. In spite of many very interesting demonstrations of feasibility in laboratories, numerous attempts to apply the technology to manufacturing conditions have not been very successful. The main objectives of this brief review are to summarize the key points of various published reports and to discuss the critical technical issues which are hindering transformation of the laboratory results to more broadly applicable technology. The vibration signals from metal cutting processes contain very useful information and offer excellent possibilities for in-process diagnosis of many critical metal cutting problems including tool wear. But the current state of knowledge still consists mainly of empirical observations, many of which need further clarification. Sonna of these key issues requiring future studies, particularly those issues related to in-process monitoring of tool wear, are discussed in this review.     

基于卷积门控循环神经网络的刀具磨损状态监测

针对刀具磨损状态在线监测需求,提出一种基于卷积门控循环神经网络的刀具磨损状态在线监测方法。综合卷积神经网络和门控循环神经网络的优点,构建了卷积门控循环神经网络;以切削力为输入信号,通过小波变换滤除噪声;利用卷积神经网络提取表征刀具磨损状态关键信息的高维特征;通过门控循环神经单元使模型在时间尺度上的累积效应得到充分表达,体现磨损的时序特性。实验表明,在有限的刀具磨损数据样本条件下,通过卷积门控循环神经网络进行刀具磨损状态监测具有较好的效果,其准确率达到97%。     

Book review of Implementing Virtual Teams by A. Edwards and J.R. Wilson,Gower Publishing Ltd,Aldershot, 2004, pages xvi+190, £49.95, hardback (ISBN 0 566 08468 6)

Tool wear monitoring and estimation are essential for improved productivity of manufacturing systems. Multi-sensory approaches based on force, vibration and Acoustic Emission (AE) signals have been recognized as potential methods for tool wear monitoring. In the present work, steady-state components of force, dynamics of the main cutting force and vibration in the direction of the main cutting force have been used for on-line tool wear estimation in a turning process. The group method of data handling (GMDH), a heuristic self-organizing method of modelling, has been used to integrate information from different sensors and the cutting conditions to obtain estimates of tool wear. Different methods of preprocessing the forces have been attempted to determine the best method to suit the data. Various heuristics of GMDH have been analysed to obtain the appropriate models for tool wear estimation. The results show that GMDH can be effectively used to integrate sensor information and obtain reliable estimates of tool wear.     

Fuzzy controlled backpropagation neural network for tool condition monitoring in face milling

The performance of a fuzzy controlled backpropagation neural network has been studied to predict the tool wear in a face milling process based on simple process parameters and sensor signal features. The results show the potentiality of the method in comparison to the standard backpropagation neural network and one of its variants. The speed of convergence, accuracy of prediction and total time of system development make fuzzy controlled backpropagation an attractive technique amenable for online tool condition monitoring.     

不同冷却润滑方式对切削SiCP/Al复合材料刀具磨损的影响

为探究不同冷却润滑方式对切削SiC_P/Al复合材料刀具磨损的影响,进行了干切削（Dry）、微量润滑（MQL）、液氮（LN₂）、切削油（Oil）和乳化液（Emulsion）共五种冷却润滑条件下的车削实验,分析了冷却润滑方式对刀具边界磨损、刀具破损和后刀面磨损的影响。结果表明:MQL和LN₂有更佳的流体冲刷效果,可以将脱落的SiC颗粒及时带离切削区,减少边界磨损; Oil和Emulsion冲刷效果较差,会加剧边界磨损。LN₂的使用会增加刀具受到的热应力和机械冲击,积屑瘤发生完全脱落,造成切削过程不平稳,当切削距离达到1 100 m时,刀具发生破损; Oil切削时,严重的边界磨损导致刀尖部位尺寸减小,强度降低,当切削距离达到825 m时发生了刀具破损。MQL良好的润滑渗透性和LN₂有效的冷却效果可以减少后刀面磨损。因此,MQL兼具冷却、润滑和流体冲刷效果,更加适合作为切削SiC_P/Al复合材料的冷却润滑方式。      

Magnetorheological fluid-based nanotexturing of tool inserts for turning of duplex stainless steel

This paper deals with the study of the nanotexturing process of the cutting tool inserts with the influence of a magnetorheological fluid-based texturing method. The rake and flank surface of the cutting tool inserts were finished with a silicon carbide abrasive mixture of a magnetorheological fluid. Experimentation is conducted with input variables such as voltage, gap width, and polishing time to achieve the desired value of % reduction of surface roughness, polishing rate, andpolishing time. The surface roughness is found to be less than 40?nm for textured and 120?nm for non-textured inserts with a lesser polishing time. A higher polishing rate of the cutting tool inserts is achieved at a working voltage of 36?V and a gap width of 0.75?mm. The machinability characteristics of the nanotextured inserts are based on the cutting force; tool wear is studied for the turning operation of Duplex stainless steel. The tool flank wear is observed to be 0.63?mm, after 13th pass when turned with an unpolished insert and 0.612?mm after the 19th pass with a polished insert. From the results, it is found that the nanotextured inserts could achieve a tool life of 60% higher than the un-textured inserts in machining the duplex stainless steel.     

Study of milling force variation in ultrasonic vibration-assisted end milling

Ultrasonic vibration cutting has been proved to be an effective cutting technology for its excellent cutting performance and has been widely applied in turning and drilling process. However, this kind of technology is rarely tried in milling process. In cutting process, cutting force is an important process parameter, which affects surface finish and tool wear. This paper investigates the milling force variation in ultrasonic vibration-assisted end milling process through a series of slot-milling experiments. The main research contents include two parts, one is the effect of the externally excited vibration on milling force in milling process, and the other is the influence of milling and vibrating parameters matching on milling force value. Experimental results show that ultrasonic vibration can change traditional milling conditions, realize separate-type milling, obtain similar pulse-like profiles of cutting forces, reduce average cutting force value; and the peak value of the feed direction cutting force can also be greatly decreased by adopting reasonable vibration amplitude, an optimal combination of machining parameters is of great benefit to achieving small cutting force. According to the experimental findings, ultrasonic vibration-assisted milling is a prospective technology to achieve precision milling of small part.     

Tool Wear Monitoring in Drilling Using Multiple Feature Fusion of the Cutting Force

1. IntroductionTool wear brings about vibration of the machine tool and deterioration of surface roughness and dimensional accuracy of the workpiece, and even causes tool damage and workpiece damage as well as downtime of the machine tool when the tool is badly worn. It is reported that 75% of all the downtime of equipment is owed to tool failure in production. So on-line monitoring of tool condition and tool wear is a key technique that realizes high efficiency and automation of the machining…     

铝基碳化硅精密铣削刀具磨损实验研究

针对铝基碳化硅切削加工中刀具易磨损、寿命低、切削难度大和加工成本高等问题,选用不同材料的硬质合金铣刀及金刚石铣刀进行切削加工实验,并利用扫描电镜和工具显微镜对高体积分数铝基碳化硅铣削时刀具磨损形态进行了分析研究.研究表明:硬质合金刀具前刀面和刃口磨损主要形式为粘结磨损和微崩刃,后刀面磨损主要为刻划磨损,而金刚石铣刀加工时刀具磨损很小;YG6X铣刀材料微观组织致密,抗磨损能力较强,宜粗加工时选用;金刚石刀体的硬度远大于SiC颗粒,且金刚石与工件的摩擦系数小,金刚石铣刀寿命远大于硬质合金铣刀,宜精加工时选用.     

Flank milling model for tool path programming of turbine blisks and compressors

In this paper, a methodology for complex surface machining based on cutting forces prediction is presented. The work is focused on blade finishing operations. The cutting forces model developed can be applied to three axis and five axis milling cases. For three-axis cases, the chip thickness is calculated according to traditional analytical methods. On the contrary, for five-axis cases the chip thickness is obtained from a geometric method developed in the paper. The cutting forces values can be calculated for the complete toolpath, but the presented model can also provide the programmer information about the cutting forces in a single point of the toolpath. The cutting force model is integrated in the CAM software in order to provide an extra tool that helps the programmer to decide which the optimal milling strategy is, based on the minimum cutting forces. In the last section, results of a case study based on impeller and blisk blades flank milling are discussed. Model predicted forces and real measured forces of flank milling operations are compared for model validation. Applying this methodology, cutting forces can be taken into account as a decisive criterion for optimal tool path selection.     

Integrated in-process chatter monitoring and automatic suppression with adaptive pitch control in parallel turning

Simultaneous processes such as parallel turning or milling offer great opportunities for more efficient manufacturing because of their higher material removal rates. To maximize their advantages, chatter suppression technologies for simultaneous processes must be developed. In this study, we constructed an automatic chatter suppression system with optimal pitch control for sharedsurface parallel turning with rigid tools and a flexible workpiece, integrating in-process chatter monitoring based on the cutting force estimation. The pitch angle between two tools is tuned adaptively in a position control system in accordance with the chatter frequency at a certain spindle speed, in a similar manner as the design methodology for variable-pitch cutters. The cutting force is estimated without using an additional external sensor by employing a multi-encoder-based disturbance observer. In addition, the chatter frequency is measured during the process by performing a low-computational-load spectrum analysis at a certain frequency range, which makes it possible to calculate the power spectrum density in the control system of the machine tool. Thus, the constructed system for automatic chatter suppression does not require any additional equipment.The full text can be downloaded at https://link.springer.com/content/pdf/10.1007%2Fs40436-018-0222-0.pdf