共查询到20条相似文献,搜索用时 15 毫秒
1.
A. Siddhpura R. Paurobally 《The International Journal of Advanced Manufacturing Technology》2013,65(1-4):371-393
Flank wear is the most commonly observed and unavoidable phenomenon in metal cutting which is also a major source of economic loss resulting due to material loss and machine down time. A wide variety of monitoring techniques have been developed for the online detection of flank wear. In order to provide a broad view of flank wear monitoring techniques and their implementation in tool condition monitoring system (TCMS), this paper reviews three key features of a TCMS, namely (1) signal acquisition, (2) signal processing and feature extraction, and (3) artificial intelligence techniques for decision making. 相似文献
2.
Somkiat Tangjitsitcharoen Haruetai Lohasiriwat 《The International Journal of Advanced Manufacturing Technology》2018,99(9-12):2219-2230
In order to realize an intelligent CNC machine, this research proposed the in-process tool wear monitoring system regardless of the chip formation in CNC turning by utilizing the wavelet transform. The in-process prediction model of tool wear is developed during the CNC turning process. The relations of the cutting speed, the feed rate, the depth of cut, the decomposed cutting forces, and the tool wear are investigated. The Daubechies wavelet transform is used to differentiate the tool wear signals from the noise and broken chip signals. The decomposed cutting force ratio is utilized to eliminate the effects of cutting conditions by taking ratio of the average variances of the decomposed feed force to that of decomposed main force on the fifth level of wavelet transform. The tool wear prediction model consists of the decomposed cutting force ratio, the cutting speed, the depth of cut, and the feed rate, which is developed based on the exponential function. The new cutting tests are performed to ensure the reliability of the tool wear prediction model. The experimental results showed that as the cutting speed, the feed rate, and the depth of cut increase, the main cutting force also increases which affects in the escalating amount of tool wear. It has been proved that the proposed system can be used to separate the chip formation signals and predict the tool wear by utilizing wavelet transform even though the cutting conditions are changed. 相似文献
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Wan-Hao Hsieh Ming-Chyuan Lu Shean-Juinn Chiou 《The International Journal of Advanced Manufacturing Technology》2012,61(1-4):53-61
This study develops a micro-tool condition monitoring system consisting of accelerometers on the spindle, a data acquisition and signal transformation module, and a backpropagation neural network. This study also discusses the effect of the sensor installations, selected features, and the bandwidth size of the features on the classification rate. To collect the vibration signals necessary for training the system model and verifying the system, an experiment was implemented on a micro-milling research platform along with a 700?μm diameter micro-end mill and a SK2 workpiece. A three-axis accelerometer was installed on a sensor plate attached to the spindle housing to collect vibration signals in three directions during cutting. The frequency domain features representing changes in tool wear were selected based on the class mean scatter criteria after transforming signals from the time domain to the frequency domain by fast Fourier transform. Using the appropriate vibration features, this study develops and tests a backpropagation neural network classifier. Results show that proper feature extraction for classification provides a better solution than applying all spectral features into the classifier. Selecting five features for classification provides a better classification rate than the case with four and three features along with the 30?Hz bandwidth size of the spectral feature. Moreover, combining the signals for tool condition from both direction signals provides a better classification rate than determining the tool condition using a one-direction single sensor. 相似文献
5.
Samir Khamel Nouredine Ouelaa Khaider Bouacha 《Journal of Mechanical Science and Technology》2012,26(11):3605-3616
The main of the present study is to investigate the effects of process parameters (cutting speed, feed rate and depth of cut) on performance characteristics (tool life, surface roughness and cutting forces) in finish hard turning of AISI 52100 bearing steel with CBN tool. The cutting forces and surface roughness are measured at the end of useful tool life. The combined effects of the process parameters on performance characteristics are investigated using ANOVA. The composite desirability optimization technique associated with the RSM quadratic models is used as multi-objective optimization approach. The results show that feed rate and cutting speed strongly influence surface roughness and tool life. However, the depth of cut exhibits maximum influence on cutting forces. The proposed experimental and statistical approaches bring reliable methodologies to model, to optimize and to improve the hard turning process. They can be extended efficiently to study other machining processes. 相似文献
6.
The effects of various factors on the three main types of tool wear were investigated experimentally. A fourth type of tool wear, nose wear, is included in flank wear and the experimental results show that it is larger than crater wear. Several important results on the effects of the factors investigated are obtained. 相似文献
7.
Jianfei Dong K. V. R. Subrahmanyam Yoke San Wong Geok Soon Hong A. R. Mohanty 《The International Journal of Advanced Manufacturing Technology》2006,30(9-10):797-807
This paper introduces the application of neural networks based on Bayesian inference, the automatic relevance determination algorithm for selecting relevant features and designing neural estimators for tool wear estimation in face-milling processes. Two types of neural networks are studied: Bayesian support vector machines for regression (BSVR) and Bayesian multilayer perceptrons (BMLP). Sixteen well-known features derived from pre-processing of the milling force signal are considered. The force signal samples are collected from 20 milling experiments under various machining conditions. The feature extraction and selection procedure is then applied to the sampled force signal. The feature selection results from the two neural networks are found to be quite similar. The average force has been proven to be the most relevant feature for tool wear estimation in both cases, among a set of six other features in each case, with each set differing by only one feature. The comparison among the generalization capabilities of the entire, selected, and rejected features shows that the selected features are relatively more relevant to tool wear processes in both cases. The comparison between the estimation results from the two neural networks using the corresponding relevant feature set shows that the BSVR method is more accurate in estimating flank wear than BMLP, but at the cost of a higher computing load. 相似文献
8.
Analytical prediction of cutting tool wear 总被引:2,自引:0,他引:2
An analytical method is presented which enables the crater and flank wear of tungsten carbide tools to be predicted for a wide variety of tool shapes and cutting conditions in practical turning operations based only on orthogonal cutting data from machining and two wear characteristic constants. A wear characteristic equation is first derived theoretically and verified experimentally. An energy method is developed to predict chip formation and cutting forces in turning with a single-point tool from the orthogonal cutting data. Using these predicted results, stress and temperature on the wear faces can be calculated. Computer simulation of the development of wear is then carried out by using the characteristic equation and the predicted stresses and temperatures upon the wear faces. The predicted wear progress and tool life are in good agreement with experimental results. 相似文献
9.
Dilbag Singh P. Venkateswara Rao 《The International Journal of Advanced Manufacturing Technology》2010,50(5-8):479-493
Due to technical and economical factors, hard turning is competing successfully with the grinding process in the industries. Many practical applications require components to be hardened in order to improve their wear behavior. Higher productivity and good surface quality are the requirements of the modern industries. However, tool wear is the major problem in hard turning. The tool wear models, used to assess the performance of hard turning process, play an important role in predicting the surface quality. So, in the present work, an attempt has been made to develop an analytical tool wear model for the mixed ceramic inserts during the hard turning of bearing steel incorporating abrasion, adhesion, and diffusion wear mechanisms. The new model developed can reliably be used to assess the wear of the mixed ceramic tools within the domain of the parameters. It has been observed that tool wear is increasing with the increase in cutting speed, feed, and effective rake angle. However, it has been found to be slightly decreasing with the increase in nose radius. The proposed model was validated by conducting experiments. It could be seen that the model was capable of predicting the flank wear using the cutting parameters and tool geometry. 相似文献
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Modelling of CBN tool crater wear in finish hard turning 总被引:1,自引:2,他引:1
Yong Huang Steven Y. Liang 《The International Journal of Advanced Manufacturing Technology》2004,24(9-10):632-639
The wear of cubic boron nitride (CBN) cutters, commonly used now in the finish turning of hardened parts, is an important issue that needs to be addressed for hard turning to be a viable technology due to the high costs of CBN cutters and the down-time for tool change. Chipping and tool breakage, which lead to early tool failure, are both prone to take place under the effect of crater wear. The objective of this study is to develop a methodology to model the CBN tool crater wear rate to both guide the design of CBN tool geometry and optimise cutting parameters in finish hard turning. First, the wear volume losses due to the main wear mechanisms (abrasion, adhesion, and diffusion) are modelled as functions of cutting temperature, stress, and other process attributes respectively. Then, the crater wear rate is predicted in terms of tool/work material properties and cutting configuration. Finally, the proposed model is experimentally validated in finish turning of hardened 52100 bearing steel using a low CBN content insert. The comparison between the prediction and the measurement shows reasonable agreement and the results suggest that adhesion is the main wear mechanism over the investigated range of cutting conditions . 相似文献
13.
《Wear》1996,193(1):16-24
Wear surfaces of the cutting tools are analyzed to study the wear mechanism of cemented carbide tools in turning in Inconel 718 superalloys. SEM and EPMA analyses indicated that the wear of carbide tools during high speed turning condition (V = 35 m min−1) was caused by diffusion of elements (Ni or Fe) in workpiece into tool's binder (Co) by a grain boundary diffusion mechanism. This action weakened the bonding strength between carbide particles (WC, TiC, TaC) and the binder (Co). The carbide particles were then detached out of the cemented carbide tool by high flow stresses. The proposed grain boundary diffusion mechanism is also confirmed by theoretical analysis. 相似文献
14.
This study establishes an analytical basis for the prediction of chatter stability in the turning process in the presence of wear flat on the tool flank. The components contributing to the forcing function in the machine vibration dynamics are analyzed in the context of cutting force, contact force and Coriolis force. In this way, the effects of the displaced workpiece volume at the wear flat as well as the workpiece rotation in conjunction with its radial compliance can be incorporated in describing the motion of the vibration system. Laplace domain analysis provides the analytical solution for the limits of stability in terms of the machine characteristics, structural stiffness, cutting stiffness, specific contact force, cutting conditions and cutter geometry. Stability plots are presented to relate stiffness ratio to cutting velocity in the determination of chatter stability. Machining experiments at various cutting conditions were conducted to identify the characteristic parameters involved in the vibration system and to verify the analytical stability limits. The extent of tool wear effect and Coriolis effect on the stability of machining is discussed. 相似文献
15.
2D FEM estimate of tool wear in turning operation 总被引:2,自引:0,他引:2
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. 相似文献
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T. Tamizharasan T. Selvaraj A. Noorul Haq 《The International Journal of Advanced Manufacturing Technology》2006,28(7-8):671-679
Hard turning is a profitable alternative to finish grinding. The ultimate aim of hard turning is to remove work piece material
in a single cut rather than a lengthy grinding operation in order to reduce processing time, production cost, surface roughness,
and setup time, and to remain competitive. In recent years, interrupted hard turning, which is the process of turning hardened
parts with areas of interrupted surfaces, has also been encouraged. The process of hard turning offers many potential benefits
compared to the conventional grinding operation. Additionally, tool wear, tool life, quality of surface turned, and amount
of material removed are also predicted. In this analysis, 18 different machining conditions, with three different grades of
polycrystalline cubic boron nitride (PCBN), cutting tool are considered. This paper describes the various characteristics
in terms of component quality, tool life, tool wear, effects of individual parameters on tool life and material removal, and
economics of operation. The newer solution, a hard turning operation, is performed on a lathe. In this study, the PCBN tool
inserts are used with a WIDAX PT GNR 2525 M16 tool holder. The hardened material selected for hard turning is commercially
available engine crank pin material. 相似文献
18.
Tae Jo Ko Dong Woo Cho 《The International Journal of Advanced Manufacturing Technology》1996,12(1):5-13
An adaptive signal processing scheme that uses a low-order autoregressive time series model is introduced to model the cutting force data for tool wear monitoring during face milling. The modelling scheme is implemented using an RLS (recursive least square) method to update the model parameters adaptively at each sampling instant. Experiments indicate that AR model parameters are good features for monitoring tool wear, thus tool wear can be detected by monitoring the evolution of the AR parameters during the milling process. The capability of tool wear monitoring is demonstrated with the application of a neural network. As a result, the neural network classifier combined with the suggested adaptive signal processing scheme is shown to be quite suitable for in-process tool wear monitoring 相似文献
19.
《Wear》2007,262(3-4):340-349
Nanometrically smooth infrared silicon optics can be manufactured by the diamond turning process. Due to its relatively low density, silicon is an ideal optical material for weight sensitive infrared (IR) applications. However, rapid diamond tool edge degradation and the effect on the achieved surface have prevented significant exploitation. With the aim of developing a process model to optimise the diamond turning of silicon optics, a series of experimental trials were devised using two ultra-precision diamond turning machines. Single crystal silicon specimens (1 1 1) were repeatedly machined using diamond tools of the same specification until the onset of surface brittle fracture. Two cutting fluids were tested. The cutting forces were monitored and the wear morphology of the tool edge was studied by scanning electron microscopy (SEM).The most significant result showed the performance of one particular tool was consistently superior when compared with other diamond tools of the same specification. This remarkable tool performance resulted in doubling the cutting distance exhibited by the other diamond tools. Another significant result was associated with coolant type. In all cases, tool life was prolonged by as much as 300% by using a specific fluid type.Further testing led to the development of a novel method for assessing the progression of diamond tool wear. In this technique, the diamond tools gradual recession profile is measured by performing a series of plunging cuts. Tool shape changes used in conjunction with flank wear SEM measurements enable the calculation of the volumetric tool wear rate. 相似文献
20.
Minimal quantity lubrication in turning: Effect on tool wear 总被引:2,自引:0,他引:2
Industries and researchers are trying to reduce the use of coolant lubricant fluids in metal cutting to obtain safety, environmental and economical benefits. The aim of this research is to determine if the minimal quantity lubrication (MQL) technique in turning gives some advantages in terms of tool wear reduction. This paper reports the results obtained from turning tests and SEM analysis of tools, at two feed rates and two cutting lengths, using MQL on the rake and flank of the tool. The results obtained show that when MQL is applied to the tool rake, tool life is generally no different from dry conditions, but MQL applied to the tool flank can increase tool life. 相似文献