Investigations into the effect of tool shapes with size factor consideration in sink electrical discharge machining (EDM) process

In sink electric discharge machining (EDM) process, the tool shape and size along with wear are of great importance because they adversely affect the accuracy of machined features. This paper presents the application of response surface methodology (RSM) for investigating the effect of tool shapes such as triangular, square, rectangular, and circular with size factor consideration along with other process parameters like discharge current, pulse on-time, pulse off-time, and tool area. The RSM-based mathematical models of material removal rate (MRR) and tool wear rate (TWR) have been developed using the data obtained through central composite design. The analysis of variance was applied to verify the lack of fit and adequacy of the developed models. Further, the confirmation tests were performed to ascertain the accuracy of the developed models. The investigations revealed that the best tool shape for higher MRR and lower TWR is circular, followed by triangular, rectangular, and square cross sections. From the parametric analysis, it is also observed that the interaction effect of discharge current and pulse on-time is highly significant on MRR and TWR, whereas the main factors such as pulse off-time and tool area are statistically significant on MRR and TWR.     

磨削过程计算机集成智能监控系统

利用神经网络建立了磨削过程计算机集成智能监控系统,该系统将磨削过程监测、故障诊断和反馈控制组成一个有机整体.通过在线提取磨削声发射(AE)信号RMS峰值、FFT峰值、信号标准偏差以及信号累积幅值增量,可以实现磨削烧伤、磨削颤振、砂轮钝化等故障的在线实时诊断.通过反馈控制系统实现磨削参数的实时调整,提高了磨削加工的性能、效率和磨削质量的稳定性.实测结果证明了该系统的有效性.     

Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling

Influence of tool geometry on the quality of surface produced is well known and hence any attempt to assess the performance of end milling should include the tool geometry. In the present work, experimental studies have been conducted to see the effect of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on the machining performance during end milling of medium carbon steel. The first and second order mathematical models, in terms of machining parameters, were developed for surface roughness prediction using response surface methodology (RSM) on the basis of experimental results. The model selected for optimization has been validated with the Chi square test. The significance of these parameters on surface roughness has been established with analysis of variance. An attempt has also been made to optimize the surface roughness prediction model using genetic algorithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions.     

CHMM for tool condition monitoring and remaining useful life prediction

To solve the problems of tool condition monitoring and prediction of remaining useful life, a method based on the Continuous Hidden Markov Model (CHMM) is presented. With milling as the research object, cutting force is taken as the monitoring signal, analyzed by wavelet packet theory to reduce noise and extract the energy feature of the signal as a basis for diagnosis. Then, CHMM is used to diagnose tool wear state. Finally, a Gaussian regression model is proposed to predict the milling tool’s remaining useful life after the test sample data are verified to be consistent with the Gaussian distribution based on a reliable identification of the milling tool wear state. The probability models of tool remaining useful life prediction could be established for tools with different initial states. For example, when an unknown state of milling force signal is delivered to the milling tool online diagnostic system, the state and the existing time of this state could be predicted by the established prediction model, and then, the average remaining useful life from the present state to the tool failure state could be obtained by analyzing the transfer time between each state in the CHMM. Compared to the traditional probabilistic model, which requires a large amount of test samples, the experimental cost is effectively reduced by applying the proposed method. The results from the experiment indicate that CHMM for tool condition monitoring has high sensitivity, requires less training samples and time, and produces results quickly. The method using the Gaussian process to accurately predict remaining life has ample potential for application to real situations.     

Online monitoring and measurements of tool wear for precision turning of stainless steel parts

Online monitoring and measurements of tool wear were carried out using cutting forces for precision turning of stainless steel parts. The best combination of features was selected from 14 features extracted from force signals by using a Sequential Forward Search algorithm. Back-propagation neural networks (BPNs) used two features for online classification. When the adaptive neuro-fuzzy inference system (ANFIS) was applied, seven features were needed for the classification. For online measurements, only one feature is needed for BPN. Three features are needed for ANFIS for online measurements. For online classification of turning tool conditions, a 2?×?20?×?1 BPN can achieve a success rate of higher than 86% while a 7?×?2 ANFIS can reach a success rate of higher than 96%. For online measurements of tool wear, the estimation error can be as low as 1.37% when a 1?×?20?×?1 BPN was used while the error can be as low as 0.56% using a 3?×?3 ANFIS. Therefore, the 3?×?3 ANFIS can be used first to predict the degradation of tool conditions during the turning process. It can also be used to measure the tool wear online so as to take feedback control action to enhance accuracy of the process. Once the detected tool wear is close to the worn-out threshold, the 7?×?2 ANFIS will be then applied to classify the tool conditions in order to stop the turning operation on time automatically so as to assure the quality of products and to avoid catastrophic failure.     

异形螺杆数控铣削刀具磨损智能建模研究

介绍了一种在线估算螺杆数控铣削中刀具磨损量的新方法。该方法基于螺杆铣削过程变切削参数的工况,提取了振动信号和功率信号的刀具磨损特征值,基于自适应神经—模糊推理系统建立了刀具磨损数学模型。实验证明,由此建立的刀具磨损模型能够排除切削参数变化的干扰,可以较好的反映加工中刀具磨损状态,同时也为具有时变切削参数特性的加工过程刀具磨损状态监控提供了新的研究方法。     

Surface roughness prediction for the milling of Ti–6Al–4V ELI alloy with the use of statistical and soft computing techniques

This study focuses on Ti–6Al–4V ELI titanium alloy machining by means of plain peripheral down milling process and subsequent modeling of this process, in order to predict surface quality of the workpiece and identify optimal cutting parameters, that lead to minimum surface roughness. For the purpose of accomplishing this task a set of experiments were performed on a CNC milling centre and design of experiments based on Box Behnken Design (BBD) for a three factor and three level central composite design concept was conducted. Depth of cut, cutting speed and feed rate were selected as input parameters and surface roughness was measured after each experiment performed. At first, Response Surface Methodology (RSM) was employed for establishing a quadratic relationship between input and output parameters. Analysis of variance (ANOVA) was then conducted for the evaluation of the proposed formula. RSM was also used for the optimization analysis that followed for the determination of milling cutting parameters for minimum surface roughness. The analysis indicates that the use of BBD can reduce the number of experiments needed for modeling and optimizing the milling operation of Titanium alloys. Furthermore, this method is able to provide models that can reliably be used for any cutting conditions within the limits of the input data. Finally, Artificial Neural Networks (ANN) models were developed to allow for a more robust simulation model to be built and comparison between ANN and RSM models to be performed. From the presented results, for RSM, the mean square error and the correlation coefficient were determined to be 8.633 × 10⁻³ and 0.9713, respectively; for ANN models, the corresponding values were 2 × 10⁻³ and 0.9824, for the test group of the optimum model. Simulations indicated that, although input data were too few, a considerably reliable ANN model was able to be built and despite of its complexity compared to RSM model, it was proven to be superior in terms of prediction accuracy.     

Analysis of acoustic emission in precision and high-efficiency grinding technology

Current demands of machining hard and brittle materials at very small tolerances have predicated the need for precision and high-efficiency grinding. In situ monitoring systems based on acoustic emission (AE) provide a new way to control the surface damage and integrality of the components. However, a high degree of confidence and reliability in characterizing the manufacturing process is required for AE to be utilized as a monitoring tool. The authors established AE based online monitoring system and studied technique parameters versus the waveforms of AE under different working conditions. The results show that there are obvious mapping relations between the technique parameters of grinding and the effective values of the AE signals. Grinding along different directions would result in different strength of AE signal. Comparing with grinding along first longitude, fewer AE signal is released when grinding along latitude and better surface quality is generated. Similar variation tendency is observed no matter between AE root mean square (RMS) and linear speed or between surface roughness and linear speed which justify some kind of correlation may exist between AE RMS and surface roughness. The distance between the AE transducer and the AE source should be less than 80 mm while monitoring the process of grinding composite ceramics.     

复杂曲面加工过程中铣刀在线监测方法

在验证了铣削力与刀柄摆动电涡流位移信号之间存在线性关系的基础上,以机床主轴端部x,y方向的加速度信号二次频域积分结果作为刀柄摆动位移信号,提取积分位移信号的基频及其谐波信号作为监测信号,解决了电涡流位移传感器安装不便的问题,同时有效去除了干扰信号的影响。利用时域同步平均(time synchronous averaging,简称TSA)计算监测信号的一阶和二阶累积量,结合时域指标方差、偏斜度、峭度、绝对均值及有效值定量刻画累积量波形,通过设定阈值实现状态预警,较好地解决了复杂曲面加工过程中铣刀状态在线监测与预警的难题。     

Prediction of quality responses in micro-EDM process using an adaptive neuro-fuzzy inference system (ANFIS) model

In the present trend of technological development, micro-machining is gaining popularity in the miniaturization of industrial products. In this work, a hybrid process of micro-wire electrical discharge grinding and micro-electrical discharge machining (EDM) is used in order to minimize inaccuracies due to clamping and damage during transfer of electrodes. The adaptive neuro-fuzzy inference system (ANFIS) and back propagation (BP)-based artificial neural network (ANN) models have been developed for the prediction of multiple quality responses in micro-EDM operations. Feed rate, capacitance, gap voltage, and threshold values were taken as the input parameters and metal removal rate, surface roughness and tool wear ratio as the output parameters. The results obtained from the ANFIS and the BP-based ANN models were compared with observed values. It is found that the predicted values of the responses are in good agreement with the experimental values and it is also observed that the ANFIS model outperforms BP-based ANN.     

Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system

One of the biggest problems in manufacturing is the failure of machine tools due to loss of surface material in cutting operations like drilling and milling. Carrying on the process with a dull tool may damage the workpiece material fabricated. On the other hand, it is unnecessary to change the cutting tool if it is still able to continue cutting operation. Therefore, an effective diagnosis mechanism is necessary for the automation of machining processes so that production loss and downtime can be avoided. This study concerns with the development of a tool wear condition-monitoring technique based on a two-stage fuzzy logic scheme. For this, signals acquired from various sensors were processed to make a decision about the status of the tool. In the first stage of the proposed scheme, statistical parameters derived from thrust force, machine sound (acquired via a very sensitive microphone) and vibration signals were used as inputs to fuzzy process; and the crisp output values of this process were then taken as the input parameters of the second stage. Conclusively, outputs of this stage were taken into a threshold function, the output of which is used to assess the condition of the tool.     

Application of ANFIS to predict crop yield based on different energy inputs

In this paper, adaptive neuro-fuzzy inference system (ANFIS) was used to predict the grain yield of irrigated wheat in Abyek town of Ghazvin province, Iran. Due to large number of inputs (eight inputs) for ANFIS, the input vector was clustered into two groups and two networks were trained. Inputs for ANFIS 1 were diesel fuel, fertilizer and electricity energies and for ANFIS 2 were human labor, machinery, chemicals, water for irrigation and seed energies. The RMSE and R² values were found 0.013 and 0.996 for ANFIS 1 and 0.018 and 0.992 for ANFIS 2, respectively. These results showed that ANFIS 1 and ANFIS 2 could well predict the yield. Finally, the predicted values of the two networks were used as inputs to the third ANFIS. The results indicated that the energy inputs in ANFIS 1 have a greater impact on the final yield production than other energy inputs. Also, the RMSE and R² values for ANFIS 3 were 0.013 and 0.996, respectively. These results showed that ANFIS 1 and the combined network (ANFIS 3) could both predict the grain yield with good accuracy.     

Artificial intelligent modeling to predict tensile strength of inertia friction-welded pipe joints

Adaptive neural network-based fuzzy inference system (ANFIS) is an artificial intelligent neuro-fuzzy technique used for modeling and control of ill-defined and uncertain systems. The present paper proposes this novel technique of ANFIS to predict the tensile strength of inertia friction-welded tubular pipe joints with the aid of artificial neural network approach combined with the principle of fuzzy logic. The proposed model is multiple input–single output type of model which uses rotational speed and forge load as input signals. The set of rules has been generated directly from the experimental data using ANFIS. The performance of the proposed model is validated by comparing the predicted results with the actual practical results obtained by conducting the confirmation experiments. The application of χ ² test confirms that the values of tensile strength predicted by proposed ANFIS model are well in agreement with the experimental values at 0.1 % level of significance. The proposed model can also be used as intelligent online adaptive control model for pipeline welding.     

Envelope dynamic analysis: a new approach for milling process monitoring

Vibration analysis has long been used for the detection and identification of the condition of machine tools. This paper proposes a method for vibration analysis in order to monitor online the milling process quality based on synchronous envelope analysis. Adapting envelope spectral analysis to characterize the milling tool is an important contribution for qualitative and quantitative characterization of milling capacity. It is a stage in modeling the three-dimensional cutting process. To determine different parameters, to understand the phenomenon which takes place during the cutting process, and to validate the monitoring algorithm, it was necessary to build and to use a complex analysis system. An experimental protocol was designed and developed for the acquisition, processing, and analyzing the three-dimensional signal. The vibration envelope analysis is proposed to detect the cutting capacity of the tool with the optimization application of cutting parameters. This purpose is reached by a detailed dynamic study of the manufacturing system divided into two parts. The first one concerns the complete analysis of the machine, of the main spindle. A dynamic analysis method is developed to completely characterize the various components of machine tools. The second is concentrated on the cutting process to condition monitoring and diagnosis. The research is focused on fast Fourier transform optimization of vibration analysis and vibration synchronous envelope to evaluate the dynamic behavior of the machine/tool/workpiece.     

Explicit optimization method for cutting-screw-thread on the basis of dual-RSM

To obtain the explicit function for optimizing the cutting-screw-thread (CST) in crash, the simulations of frontal crash at the speed of 56 km/h have been carried out in VPG. The peak acceleration in crash has been taken as the evaluation index of energy absorption characteristics. First, the single factor experiment was taken based on six parameters affecting on the absorption characteristics of CST. Second, the peak acceleration function of each parameter by using response surface method (RSM) is obtained. Third, the explicit resultant peak acceleration function of six parameters by using RSM again is obtained. A dual RSM-based explicit method is proposed. According to this function, the best size dimensions of CST in different crash conditions could be easily obtained. Finally, an example shows that the values of the calculation errors for simulation value and target value (40 g) are 3.6% and 1.3%, respectively. This method can satisfy the demand for engineering accuracy.     

Towards selective laser sintering of objects with customized mechanical properties based on ANFIS predictions

Recently, the adaptive network-based fuzzy inference system (ANFIS) has been used extensively in modeling of manufacturing processes to save both optimization time and manufacturing costs. ANFIS is a powerful iterative tool for optimizing non-linear and multivariable manufacturing operations. In the present study, ANFIS is used to predict the optimum manufacturing parameters in selective laser sintering (SLS) of cement-filled polyamide 12 (PA12) composite. For this purpose, a set of cement-filled PA12 test specimens is manufactured by SLS technique with 8 different values of laser power (4.5–8 Watt) and 8 different weight fractions of white cement (5 %–40 %). Mechanical characterization of cement-filled PA12 is carried out to evaluate the ultimate tensile strength (UTS), compressive strength, and flexural properties. The experimental data are then divided into two groups; one group for training the ANFIS model and the other group for checking the validity of the identified model. The built ANFIS model was validated experimentally and comparison with experimental results revealed mean relative errors of 2.92 %, 3.84 %, 4.75 %, and 3.31 % in the predictions of UTS, compressive strength, flexural modulus, and flexural yield strength, respectively.

     

APPLICATION OF WAVELET TRANSFORM ON DIAGNOSIS AND PREDICTION OF MILLING CHATTER

In order to avoid the accuracy deterioration or tool damage caused by milling chatter, it is necessary to have an efficient and reliable diagnosis system that can on-line predict/detect the occur-rence of chatter. The diagnosis/predicting system proposed is to on-line process and analysis the vi-bration signals of the milling machine measured by accelerometers. According to the analysis results, the system will be able to detect/predict the occurrence of the chatter. The diagnosis algorithm is, first, collecting both the normal signals and chatter signals from milling processes, and then, converting the signals through wavelet transform and fast Fourier transform (FFT). Since the converted chatter sig-nals exhibit different characteristics from the normal signals, through defining the characteristic val-ues, such as root-mean-square value, max value, and ratio of peak value to root-mean-square value, etc, a diagnosis reference library that contains the distribution of these characteristic values is built for diagnosis. When a diagnosis is executing, the characteristic value of the measured signals is con-trasted with the diagnosis reference. The approach index which shows the possibility of occurrence of milling chatter will, then, be calculated through the diagnosis system. Cutting experiments are con-ducted to verify the proposed diagnosis system. The results show the success of early chatter detecting for the system.     

Fault diagnosis of the polypropylene production process (UNIPOL PP) using ANFIS

The performance of a chemical process plant can gradually degrade due to deterioration of the process equipment and unpermitted deviation of the characteristic variables of the system. Hence, advanced supervision is required for early detection, isolation and correction of abnormal conditions. This work presents the use of an adaptive neuro–fuzzy inference system (ANFIS) for online fault diagnosis of a gas-phase polypropylene production process with emphasis on fast and accurate diagnosis, multiple fault identification and adaptability. The most influential inputs are selected from the raw measured data sets and fed to multiple ANFIS classifiers to identify faults occurring in the process, eliminating the requirement of a detailed process model. Simulation results illustrated that the proposed method effectively diagnosed different fault types and severities, and that it has a better performance compared to a conventional multivariate statistical approach based on principal component analysis (PCA). The proposed method is shown to be simple to apply, robust to measurement noise and able to rapidly discriminate between multiple faults occurring simultaneously. This method is applicable for plant-wide monitoring and can serve as an early warning system to identify process upsets that could threaten the process operation ahead of time.     

基于故障先兆判定模型和动态置信度匹配的主轴润滑故障预测方法

为提高数控机床主轴传动系统润滑不良故障的预测能力和预知性维护能力,针对现有故障预测方法的局限性以及主轴零部件耦合、故障并发等特征,提出一种基于故障先兆判定模型和动态置信度匹配的主轴润滑故障预测方法。根据关联程度约简故障先兆表征参数,基于故障历史数据集定义故障先兆状态序列,结合小波分析和概率神经网络技术构建故障先兆判定模型,设计动态置信度匹配算法,进而从可靠性和正确性的角度融合各故障先兆参数状态匹配结果,在线预测故障发生的概率及时间。试验结果表明,该方法能够有效实现主轴传动系统润滑不良故障的预测。     

Multi-response optimization of EDM with Al–Cu–Si–TiC P/M composite electrode

The present study investigates the relationship of process parameters in electro-discharge of CK45 steel with novel tool electrode material such as Al–Cu–Si–TiC composite produced using powder metallurgy (P/M) technique. The central composite second-order rotatable design had been utilized to plan the experiments, and response surface methodology (RSM) was employed for developing experimental models. Analysis on machining characteristics of electrical discharge machining (EDM) die sinking was made based on the developed models. In this study, titanium carbide percent (TiC%), peak current, dielectric flushing pressure, and pulse on-time are considered as input process parameters. The process performances such as material removal rate (MRR) and tool wear rate (TWR) were evaluated. Analysis of variance test had also been carried out to check the adequacy of the developed regression models. Al–Cu–Si–TiC P/M electrodes are found to be more sensitive to peak current and pulse on-time than conventional electrodes. The observed optimal process parameter settings based on composite desirability are TiC percent of 18%, peak current of 6 A, flushing pressure of 1.2 MPa, and pulse on-time of 182 μs for achieving maximum MRR and minimum TWR; finally, the results were experimentally verified. A good agreement is observed between the results based on the RSM model and the actual experimental observations. The error between experimental and predicted values at the optimal combination of parameter settings for MRR and TWR lie within 7.2% and 4.74%, respectively.