模具钢铣削中刀具磨损的试验研究

以面铣刀刀片磨损为研究对象,结合类神经网络系统建构高速数控铣削加工的预测模型。以加工参数为模型输入条件,刀腹磨耗为输出条件。采用多因素试验方法,选择切削速度、进给速度、切削深度三个试验参数,利用直交表式的试验计划法设计试验点。依照试验点铣削工件后再测量刀具加工后的刀腹磨耗量,进而求得倒传递网络所需的36组训练范例与11组验证数据。刀腹磨耗预测模式是利用类神经网络中的倒传递网络原理,以田口法求得倒传递网络参数的最优值。试验结果显示,刀腹磨耗随着切削速度、进给速度、切削深度增加而上升。铣削模具钢后,刀具磨耗预测值的平均误差为4.72%,最大误差为11.43%,最小误差为0.31%。整体而言,类神经网络对于铣削加工可进行有效预测。     

Application of particle swarm optimisation in artificial neural network for the prediction of tool life

In an advanced manufacturing system, accurate assessment of tool life estimation is very essential for optimising the cutting performance in turning operations. Estimation of tool life generally requires considerable time and material and hence it is a relatively expensive procedure. In this present work, back-propagation feed forward artificial neural network (ANN) has been used for tool life prediction. Speed, feed, depth of cut and flank wear were taken as input parameters and tool life as an output parameter. Twenty-five patterns were used for training the network. Recently there have been significant research efforts to apply evolutionary computational techniques for determining the network weights. Hence an evolutionary technique named particle swarm optimisation has been used instead of a back-propagation algorithm and it is proven that the experimental results matched well with the values predicted by both artificial neural network with back-propagation and the proposed method. It is found that the computational time is greatly reduced by this method .     

Application of particle swarm optimisation in artificial neural network for the prediction of tool life

In an advanced manufacturing system, accurate assessment of tool life estimation is very essential for optimising the cutting performance in turning operation. Estimation of tool life generally requires considerable time and material and hence it is a relatively expensive procedure. In this present work, back-propagation feed forward artificial neural network (ANN) has been used for tool life prediction. Speed, feed, depth of cut and flank wear were taken as input parameters and tool life as an output parameter. Twenty-five patterns were used for training the network. Recently there have been significant research efforts to apply evolutionary computational techniques for determining the network weights. Hence an evolutionary technique named particle swarm optimisation has been used instead of the back-propagation algorithm and it is proved that the experimental results matched well with the values predicted by both artificial neural network with back-propagation and the proposed method. It is found that the computational time is greatly reduced by this method.     

PCD刀具高速铣削GH4169合金的刀具磨损规律研究

为研究PCD刀具高速铣削GH4169合金时刀具的磨损规律,采用单因素试验法分别对不同铣削参数下后刀面磨损程度随切削路程的变化进行对比。结果显示主轴转速对高速铣削GH4169合金时刀具磨损的影响不大,采用顺铣、切削液冷却的方式,并适当降低每齿进给量有助于减小刀具磨损。使用BP神经网络对试验数据进行训练,建立了刀具磨损预测的模型,预测结果与实际结果误差在5%以内。     

Performance prediction of high-pressure coolant assisted turning of Ti-6Al-4V

The present study focuses on the development of predictive models of average surface roughness, chip-tool interface temperature, chip reduction coefficient, and average tool flank wear in turning of Ti-6Al-4V alloy. The cutting speed, feed rate, cutting conditions (dry and high-pressure coolant), and turning forces (cutting force and feed force) were the input variables in modeling the first three quality parameters, while in modeling tool wear, the machining time was the only variable. Notably, the machining environment influences the machining performance; yet, very few models exist wherein this variable was considered as input. Herein, soft computing-based modeling techniques such as artificial neural network (ANN) and support vector machines (SVM) were explored for roughness, temperature, and chip coefficient. The prediction capability of the formulated models was compared based on the lowest mean absolute percentage error. For surface roughness and cutting temperature, the ANN and, for chip reduction coefficient, the SVM revealed the lowest error, hence recommended. In addition, empirical models were constructed by using the experimental data of tool wear. The adequacy and good fit of tool wear models were justified by a coefficient of determination value greater than 0.99.     

Optimization of turning process using artificial intelligence technology

In the current work, some experiments were performed based on a design of experiment (DOE) technique called full factorial design. The experimental results are discussed in statistical analysis, and the system was modeled using the artificial neural network (ANN) and subsequently optimized by a genetic algorithm (GA). The statistical analysis shows that the main effects and some 2-interaction effects affect the surface roughness and flank wear. The results show that the feed rate, nose radius, and approach angle have a significant effect on the flank wear and the surface roughness, but the cutting velocity has a significant effect on the flank wear alone. The optimum values of cutting parameters were identified and the resultant optimum values of flank wear and surface roughness were found to be in good agreement with the results of a validation experiment under a similar condition. The optimized values showed a significant reduction in roughness and flank wear.     

A step towards the in-process monitoring for electrochemical microdrilling

The bandsawing as a multi-point cutting operation is the preferred method for cutting off raw materials in industry. Although cutting off with bandsaw is very old process, research efforts are very limited compared to the other cutting process. Appropriate online tool condition monitoring system is essential for sophisticated and automated machine tools to achieve better tool management. Tool wear monitoring models using artificial neural network are developed to predict the tool wear during cutting off the raw materials (American Iron and Steel Institute 1020, 1040 and 4140) by bandsaw. Based on a continuous data acquisition of cutting force signals, it is possible to estimate or to classify certain wear parameters by means of neural networks thanks to reasonably quick data-processing capability. The multi-layered feed forward artificial neural network (ANN) system of a 6?×?9?×?1 structure based on cutting forces was trained using error back-propagation training algorithm to estimate tool wear in bandsawing. The data used for the training and checking of the network were derived from the experiments according to the principles of Taguchi design of experiments planned as L ₂₇. The factors considered as input in the experiment were the feed rate, the cutting speed, the engagement length and material hardness. 3D surface plots are generated using ANN model to study the interaction effects of cutting conditions on sawblade. The analysis shows that cutting length, hardness and cutting speed have significant effect on tooth wear, respectively, while feed rate has less effect. In this study, the details of experimentation and ANN application to predict tooth wear have been presented. The system shows that there is close match between the flank wear estimated and measured directly.     

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.     

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

Palanisamy et al. (Int J Adv Manuf Technol Volume 37:29–41, 2008) focus on two different models in their research, namely, regression model (RM) artificial neural network (ANN) for predicting tool wear. The prediction values from RM and ANN models are compared with the experimental values conducted by them. The discussion shall focus on the following four main points that are not considered in the study.     

线性回归模型诊断和在线预测刀具磨损量的方法研究

目的是研究诊断端面铣刀磨损量和在线预测铣刀的剩余寿命的方法.采用线性回归模型估计测刀面的磨损量.线性回归模型的输入是从铣刀受力信号提取出的特征和切削条件,比如进给量、转速等.在诊断了刀具的磨损量后,采用双指数平滑方法跟随诊断结果预测铣刀的使用寿命.最后,通过卖验验证了基于线性回归模型得到的刀具的磨损量和基于双指数平滑方法在线预测铣刀的剩余寿命的可行性.     

An analytical approach to cutting force prediction in milling of carbon fiber reinforced polymer laminates

ABSTRACT

A prediction model of cutting force for milling multidirectional laminate of carbon fiber reinforced polymer (CFRP) composites was developed in this article by using an analytical approach. In the predictive model, an equivalent uniform chip thickness was used in the case of orthogonal plane cutting, and the average specific cutting energy was taken as an empirical function of equivalent chip thickness and fiber orientation angle. The parameters in the model were determined by the experimental data. Then, the analytical model of cutting force prediction was validated by the experimental data of multidirectional CFRP laminates, which shows the good reliability of the model established. Furthermore, the cutting force component of flank contact force was correlated with the surface roughness of workpiece and the flank wear of tool in milling UD-CFRP composites. It was found that surface quality as well as flank wear has a co-incident varying trend with the flank contact force, as confirmed by the observations of the machined surfaces and tool wear at different fiber orientations. So, it can be known that low flank contact force be required to reduce surface damage and flank wear.     

Milling tool wear diagnosis by feed motor current signal using an artificial neural network

In this paper, a Multi-layer perceptron (MLP) neural network was used to predict tool wear in face milling. For this purpose, a series of experiments was conducted using a milling machine on a CK45 work piece. Tool wear was measured by an optical microscope. To improve the accuracy and reliability of the monitoring system, tool wear state was classified into five groups, namely, no wear, slight wear, normal wear, severe wear and broken tool. Experiments were conducted with the aforementioned tool wear states, and different machining conditions and data were extracted. An increase in current amplitude was observed as the tool wear increased. Furthermore, effects of parameters such as tool wear, feed, and cut depth on motor current consumption were analyzed. Considering the complexity of the wear state classification, a multi-layer neural network was used. The root mean square of motor current, feed, cut depth, and tool rpm were chosen as the input and amount of flank wear as the output of MLP. Results showed good performance of the designed tool wear monitoring system.     

Development of a cutting tool condition monitoring system for high speed turning operation by vibration and strain analysis

Cutting tool wear is a critical phenomenon which influences the quality of the machined part. In this paper, an attempt has been made to create artificial flank wear using the electrical discharge machining (EDM) process to emulate the actual or real flank wear. The tests were conducted using coated carbide inserts, with and without wear on EN-8 steel, and the acquired data were used to develop artificial neural networks model. Empirical models have been developed using analysis of variance (ANOVA). In order to analyze the response of the system, experiments were carried out for various cutting speeds, depths of cut and feed rates. To increase the confidence limit and reliability of the experimental data, full factorial experimental design (135 experiments) has been carried out. Vibration and strain data during the cutting process are recorded using two accelerometers and one strain gauge bridge. Power spectral analysis was carried out to test the level of significance through regression analysis. Experimental results were analyzed with respect to various depths of cut, feed rates and cutting speeds.     

Intelligent optimization and selection of machining parameters in finish turning and facing of Inconel 718

The heat-resistant super alloy material like Inconel 718 machining is an inevitable and challenging task even in modern manufacturing processes. This paper describes the genetic algorithm coupled with artificial neural network (ANN) as an intelligent optimization technique for machining parameters optimization of Inconel 718. The machining experiments were conducted based on the design of experiments full-factorial type by varying the cutting speed, feed, and depth of cut as machining parameters against the responses of flank wear and surface roughness. The combined effects of cutting speed, feed, and depth of cut on the performance measures of surface roughness and flank wear were investigated by the analysis of variance. Using these experimental data, the mathematical model and ANN model were developed for constraints and fitness function evaluation in the intelligent optimization process. The optimization results were plotted as Pareto optimal front. Optimal machining parameters were obtained from the Pareto front graph. The confirmation experiments were conducted for the optimal machining parameters, and the betterment has been proved.     

Performance comparison of conventional and wiper ceramic inserts in hard turning through artificial neural network modeling

Hard turning with ceramic tools provides an alternative to grinding operation in machining high precision and hardened components. But, the main concerns are the cost of expensive tool materials and the effect of the process on machinability. The poor selection of cutting conditions may lead to excessive tool wear and increased surface roughness of workpiece. Hence, there is a need to investigate the effects of process parameters on machinability characteristics in hard turning. In this work, the influence of cutting speed, feed rate, and machining time on machinability aspects such as specific cutting force, surface roughness, and tool wear in AISI D2 cold work tool steel hard turning with three different ceramic inserts, namely, CC650, CC650WG, and GC6050WH has been studied. A multilayer feed-forward artificial neural network (ANN), trained using error back-propagation training algorithm has been employed for predicting the machinability. The input?Coutput patterns required for ANN training and testing are obtained from the turning experiments planned through full factorial design. The simulation results demonstrate the effectiveness of ANN models to analyze the effects of cutting conditions as well as to study the performance of conventional and wiper ceramic inserts on machinability.     

Predicting drill wear using an artificial neural network

The present work deals with drill wear monitoring using an artificial neural network. A back propagation neural network (BPNN) has been used to predict the flank wear of high-speed steel (HSS) drill bits for drilling holes on copper work-piece. Experiments have been carried out over a wide range of cutting conditions and the effect of various process parameter like feedrate, spindle speed, and drill diameter on thrust force and torque has been studied. The data thus obtained from the experiments have been used to train a BPNN for wear prediction. The performance of the trained neural network has been tested with the experimental data, and has been found to be satisfactory.     

Experimental and finite element analysis of parameters in manufacturing of metal bellows

The current article presents an investigation into predicting tool wear in hard machining D2 AISI steel using neural networks. An experimental investigation was carried out using ceramic cutting tools, composed approximately of Al2O3 (70%) and TiC (30%), on cold work tool steel D2 (AISI) heat treated to a hardness of 60 HRC. Two models were adjusted to predict tool wear for different values of cutting speed, feed and time, one of them based on statistical regression, and the other based on a multilayer perceptron neural network. Parameters of the design and the training process, for the neural network, have been optimised using the Taguchi method. Outcomes from the two models were analysed and compared. The neural network model has shown better capability to make accurate predictions of tool wear under the conditions studied.     

Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts

In this paper, the Taguchi method and regression analysis have been applied to evaluate the machinability of Hadfield steel with PVD TiAlN- and CVD TiCN/Al₂O₃-coated carbide inserts under dry milling conditions. Several experiments were conducted using the L₁₈ (2 × 3 × 3) full-factorial design with a mixed orthogonal array on a CNC vertical machining center. Analysis of variance (ANOVA) was used to determine the effects of the machining parameters on surface roughness and flank wear. The cutting tool, cutting speed and feed rate were selected as machining parameters. The analysis results revealed that the feed rate was the dominant factor affecting surface roughness and cutting speed was the dominant factor affecting flank wear. Linear and quadratic regression analyses were applied to predict the outcomes of the experiment. The predicted values and measured values were very close to each other. Confirmation test results showed that the Taguchi method was very successful in the optimization of machining parameters for minimum surface roughness and flank wear in the milling the Hadfield steel.     

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 .     

Ti-6Al-4V合金激光辅助铣削的刀具磨损特性

对激光辅助铣削钛合金Ti-6Al-4V进行了实验研究,分析了切削力、切屑形貌和刀具的磨损特性。结果表明,与普通铣削相比,激光辅助铣削条件下,刀具切向的切削力明显减小,刀具径向的切削力略有增大;随着激光功率的增大,钛合金切屑呈现出从锯齿形向连续形过渡的特征,不再具有明显的绝热剪切带;与普通铣削时刀具崩刃的损伤不同,激光辅助铣削时刀具的磨损主要表现为后刀面磨损;激光辅助铣削可以减小后刀面的最大磨损量,但并不能改善后刀面平均磨损量。激光辅助铣削时,刀具寿命得到了延长,当后刀面的平均磨损量在0.15~0.20mm之间时,可以降低刀具的磨损速度,从而延长刀具的使用寿命,但激光辅助铣削并不能降低刀具的初期磨损速度。