APPLICATION OF ABDUCTIVE POLYNOMIAL NETWORK AND GREY THEORY TO DRILL FLANK WEAR PREDICTION

An abductive polynomial network for drill flank wear prediction was established, in which grey relational analysis was incorporated to explore the effect of various drilling parameters on flank wear. An abductive polynomial network usually includes multiple layers, each of which contains different polynomial functional nodes. It can automatically synthesize the optimal network structure, including the optimal number of layers and the optimal form of functional nodes. The correlation between the drilling input parameters, including the average thrust force, torque, cutting speed, feed and drill diameter, and drill flank wear can be achieved through this network model.

Based on experimental data, the developed network of this paper attained better accuracy in predicting drill flank wear, given the CPM of 0.1. The findings prove that the network is feasible and accurate in predicting flank wear.

In addition, grey relational analysis was used in this paper to investigate the effect of the aforementioned five drilling parameters on flank wear. According to the analytical results, the most influential factor on flank wear is drill diameter, followed by the average thrust force.     

Artificial neural network based prediction of drill flank wear from motor current signals

In this work, a multilayer neural network with back propagation algorithm (BPNN) has been applied to predict the average flank wear of a high speed steel (HSS) drill bit for drilling on a mild steel work piece. Root mean square (RMS) value of the spindle motor current, drill diameter, spindle speed and feed-rate are inputs to the network, and drill wear is the output. Drilling experiments have been carried out over a wide range of cutting conditions and the effects of drill wear, cutting conditions (speed, drill diameter, feed-rate) on the spindle motor current have been investigated. The performance of the trained neural network has been tested for new cutting conditions, and found to be in very good agreement to the experimentally determined drill wear values. The accuracy of the prediction of drill wear using neural network is found to be better than that using regression model.     

Online tool wear prediction in drilling operations using selective artificial neural network ensemble model

Online tool wear prediction plays a key role in industry automation for higher productivity and product quality. In recent past, several artificial neural network (ANN) models using multiple sensor signals as inputs for prediction as well as classification of tool wear have been proposed. However, a single ANN used in these models is often tries, which could limit their wide applications due to the complicated procedure of constructing a single ANN model. This study proposed a selective ANN ensemble approach DPSOEN, where several selected component ANNs are jointly used to online predict flank wear in drilling operation. DPSOEN provides more simple training and better generalization performance than using single ANN and hence is easier to be used by operators who often are not good at ANN techniques. Two benchmark cases were used to evaluate the performance of DPSOEN in predicting flank wear. It shows improved generalization performance that outperforms those of single ANN and Ensemble ALL approach. The investigation proposed a heuristic approach for applying the DPSOEN-based model as an effective and useful tool to predict tool wear online with potential applications for tool condition monitoring in general. Analysis from this study provides guidelines in developing ANN ensemble-based tool wear prediction systems.     

Wavelet networks for sensor signal classification in flank wear assessment

It is known that the force and vibration sensor signals in a turning process are sensitive to the gradually increasing flank wear. Based on this fact, this paper investigates a flank wear assessment technique in turning through force and vibration signals. Mainly to reduce the computational burden associated with the existing sensor-based methods for flank wear assessment, a so-called wavelet network is investigated. The basic idea in this new method is to optimize simultaneously the wavelet parameters (that represent signal features) and the signal-interpretation parameters (that are equivalent to neural network weights) to eliminate the feature extraction phase without increasing the computational complexity of the neural network. A neural network architecture similar to a standard one-hidden-layer feedforward neural network is used to relate sensor signal measurements to flank wear classes. A novel training algorithm for such a network is developed. The performance of this n ew method is compared with a previously developed flank wear assessment method which uses a separate feature extraction step. The proposed wavelet network can also be useful for developing signal interpretation schemes for manufacturing process monitoring, critical component monitoring, and product quality monitoring.     

Design of artificial neural networks for tool wear monitoring

An on-line scheme for tool wear monitoring using artificial neural networks (ANNs) has been proposed. Cutting velocity, feed, cutting force and machining time are given as inputs to the ANN, and the flank wear is estimated using the ANN. Different ANN structures are designed and investigated to estimate the tool wear accurately. An existing analytical model is used to obtain the data for various cutting conditions in order to eliminate the huge cost and time associated with generation of training and evaluation data. Motivated by the fact that the tool wear at a given instance of time depends on the tool wear value at a previous instance of time, memory is included in the ANN. ANNs without memory, with one-phase memory, and with two-phase memory are investigated in this study. The effect of various training parameters, such as learning coefficient, momentum, temperature, and number of hidden neurons, on these architectures is studied. The findings and experience obtained should facilitate the design and implementation of reliable and economical real-time systems for tool wear monitoring and identification in intelligent manufacturing.     

Drill flank wear estimation using supervised vector quantization neural networks

Drill wear detection and prognosis is one of the most important considerations in reducing the cost of rework and scrap and to optimize tool utilization in hole making industry. This study presents the development and implementation of two supervised vector quantization neural networks for estimating the flank-land wear size of a twist drill. The two algorithms are; the learning vector quantization (LVQ) and the fuzzy learning vector quantization (FLVQ). The input features to the neural networks were extracted from the vibration signals using power spectral analysis and continuous wavelet transform techniques. Training and testing were performed under a variety of speeds and feeds in the dry drilling of steel plates. It was found that the FLVQ is more efficient in assessing the flank wear size than the LVQ. The experimental procedure for acquiring vibration data and extracting features in the time-frequency domain using the wavelet transform is detailed. Experimental results demonstrated that the proposed neural network algorithms were effective in estimating the size of the drill flank wear.     

Tool condition monitoring using reflectance of chip surface and neural network

A wide variety of tool condition monitoring techniques has been introduced in recent years. Among them, tool force monitoring, tool vibration monitoring and tool acoustics emission monitoring are the three most common indirect tool condition monitoring techniques. Using multiple intelligent sensors, these techniques are able to monitor tool condition with varying degrees of success. This paper presents a novel approach for the estimation of tool wear using the reflectance of cutting chip surface and a back propagation neural network. It postulates that the condition of a tool can be determined using the surface finish and color of a cutting chip. A series of experiments has been carried out. The experimental data obtained was used to train the back propagation neural network. Subsequently, the trained neural network was used to perform tool wear prediction. Results show that the prediction is in good agreement with the flank wear measured experimentally.     

Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine

This study is deals with artificial neural network (ANN) and fuzzy expert system (FES) modelling of a gasoline engine to predict engine power, torque, specific fuel consumption and hydrocarbon emission. In this study, experimental data, which were obtained from experimental studies in a laboratory environment, have been used. Using some of the experimental data for training and testing an ANN for the engine was developed. Also the FES has been developed and realized. In this systems output parameters power, torque, specific fuel consumption and hydrocarbon emission have been determined using input parameters intake valve opening advance and engine speed. When experimental data and results obtained from ANN and FES were compared by t-test in SPSS and regression analysis in Matlab, it was determined that both groups of data are consistent with each other for p > 0.05 confidence interval and differences were statistically not significant. As a result, it has been shown that developed ANN and FES can be used reliably in automotive industry and engineering instead of experimental work.     

Evaluation of the performance of backpropagation and radial basis function neural networks in predicting the drill flank wear

This study compares the performance of backpropagation neural network (BPNN) and radial basis function network (RBFN) in predicting the flank wear of high speed steel drill bits for drilling holes on mild steel and copper work pieces. The validation of the methodology is carried out following a series of experiments performed over a wide range of cutting conditions in which the effect of various process parameters, such as drill diameter, feed-rate, spindle speed, etc. on drill wear has been considered. Subsequently, the data, divided suitably into training and testing samples, have been used to effectively train both the backpropagation and radial basis function neural networks, and the individual performance of the two networks is then analyzed. It is observed that the performance of the RBFN fails to match that of the BPNN when the network complexity and the amount of data available are the constraining factors. However, when a simpler training procedure and reduced computational times are required, then RBFN is the preferred choice.     

Drill wear sensing and failure prediction for untended machining

Real-time control of drilling was carried out by measuring the thrust force and determining its gradient. Using a microcomputer-based feedback control system, experiments were carried out under different cutting conditions to test the effectiveness of the thrust force gradient in predicting failure. The system was able to predict failure due to excessive wear commonly encountered with 5 and 8 mm drills. With such drills, excessive weat at the outer corner led to an increase in the local temperature which in turn increased the wear. This led to very high temperatures (>600°C), causing local welding of the drill material to the peripheral surface of the hole being drilled. Furthermore, the high temperatures reduced the compressive yield strength of the drill material, causing sub-surface fracture to occur under the influence of the cutting loads. This cyclic phenomenon of “seizure” due to local welding and “release” due to shear fracture (i.e. “stick-slip”) caused sharp fluctuations in the thrust force under constant feed.

This paper discusses the effectiveness of the control system described above in predicting failure due to the excessive wear common to large drills. This system is also contrasted with another based on vibration measurements which has been successfully used to predict failure due to fracture common with small drills. This paper also presents other experimental sensor schemes in the literature. Finally, this paper proposes a framework for an “intelligent” machining process control system driven by multiple sensors, which would facilitate untended machining.     

Wear State Recognition of Drills Based on/（-means Cluster and Radial Basis Function Neural Network

Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps: first, decomposing cutting torque components from the original signals by wavelet packet decomposition (WPD); second, extracting wavelet coefficients of different wear states (i.e., slight, normal, or severe wear) with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network (RBFNN). The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.     

Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network

The purpose of this article is to evaluate and predict blast-induced ground vibration at Shur River Dam in Iran using different empirical vibration predictors and artificial neural network (ANN) model. Ground vibration is a seismic wave that spreads out from the blasthole when explosive charge is detonated in a confined manner. Ground vibrations were recorded and monitored in and around the Shur River Dam, Iran, at different vulnerable and strategic locations. A total of 20 blast vibration records were monitored, out of which 16 data sets were used for training of the ANN model as well as determining site constants of various vibration predictors. The rest of the 4 blast vibration data sets were used for the validation and comparison of the result of ANN and different empirical predictors. Performances of the different predictor models were assessed using standard statistical evaluation criteria. Finally, it was found that the ANN model is more accurate as compared to the various empirical models available. As such, a high conformity (R ² = 0.927) was observed between the measured and predicted peak particle velocity by the developed ANN model.     

Soot emission prediction of a waste-gated turbo-charged DI diesel engine using artificial neural network

This study is about soot emission prediction of a waste-gated turbo-charged DI diesel engine using artificial neural network (ANN). For training the ANN model, six ranges of experimental data in previous study were used, and one range of data was kept for testing the accuracy of ANN predictions. The input parameters for the ANN are inlet manifold pressure, inlet manifold temperature, inlet air mass flow rate, fuel consumption, engine torque, and speed. Output parameter is the density of soot in the exhaust. The results show the ANN approach can be used to accurately predict soot emission of a turbo-charged diesel engine in different opening ranges of waste-gate (ORWG). Root mean-squared error (RMSE), fraction of variance (R ²), and mean absolute percentage error (MAPE) for predictions were found to be 1.19 (mg/m³), 0.9998, and 6.4%, respectively.     

A neural network approach for approximate force response analyses of a bridge population

In this paper, artificial neural networks (ANNs) are used to develop an efficient method for rapid and approximate force response analyses of a bridge population. The single-span reinforced concrete T-beam bridge population in Pennsylvania State is taken as a particular case study. First, a statistical analysis is conducted to examine implicit and explicit dependencies between various geometrical and structural parameters of the bridges, and the governing bridge parameters are identified along with their ranges of variation within the population. Then, a set of sample bridges are randomly generated using different combinations of the governing parameters within their predefined ranges of variation. An exact finite element analysis is implemented for each sample bridge, and the maximum moment and shear responses in beams are obtained at critical locations under various combinations of standard truck loads. An ANN is implemented to learn the relationship between the bridge parameters (inputs) and responses (outputs) based on the sample set and to make predictions for other bridges that are not present in the set. The performances of a variety of different ANN architectures are tested, and their prediction capabilities are measured and compared.     

Improvement of intelligent methods for evaluating the apparent quality of knitted fabrics

In the previous studies, the standard boards of yarn (ASTM) were analyzed using the image analysis method and artificial neural networks; the appearance of different knitted fabrics samples was tested for appearance. There was a strong influence of yarn type and fabrics structure on fabrics apparent quality. In the present research, the artificial neural network (ANN) has been applied to predict the fabric apparent parameters. The optimum structure of ANN has been designed using the genetic algorithm method. The results show that the ANN can be optimized very well by the GA and the designed ANN is very accurate and applicable to predict the apparent parameters.     

人工神经网络预测刀具磨损和切削力

刀具磨损和切削力预测与控制是切削加工过程中需要考虑的重要问题.本文介绍了利用人工神经网络模型预测刀具磨损和切削力的步骤并且针对产生误差的因素进行分析.首先将切削速度、切削深度、切削时间、主轴转速和不同频带的能量值通过归一化法处理,作为输入特征值,对改进的神经网络模型进行训练.然后利用训练完成的神经网络模型预测刀具磨损和切削力.结果表明:神经网络模型能够综合考虑加工过程中更多的影响因素,与经验公式结果对比,具有更高的预测精度.研究结果表明神经网络模型预测刀具磨损和切削力具有可行性和准确性,为刀具结构的优化及加工参数的选择提供了依据.     

Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks

In a modern machining system, tool condition monitoring systems are needed to get higher quality production and to prevent the downtime of machine tools due to catastrophic tool failures. Also, in precision machining processes surface quality of the manufactured part can be related to the conditions of the cutting tools. This increases industrial interest for in-process tool condition monitoring (TCM) systems. TCM supported modern unmanned manufacturing process is an integrated system composed of sensors, signal processing interface and intelligent decision making strategies. This study includes key considerations for development of an online TCM system for milling of Inconel 718 superalloy. An effective and efficient strategy based on artificial neural networks (ANN) is presented to estimate tool flank wear. ANN based decision making model was trained by using real time acquired three axis (F_x, F_y, F_z) cutting force and torque (M_z) signals and also with cutting conditions and time. The presented ANN model demonstrated a very good statistical performance with a high correlation and extremely low error ratio between the actual and predicted values of flank wear.     

Classification of sleep stages using class-dependent sequential feature selection and artificial neural network

Several studies have been conducted for automatic classification of sleep stages to ease time-consuming manual scoring process that can involve a high degree of experience and subjectivity. But none of them has found a practical usage in medical area so far because of their under acceptable success rates. In this study, a different classification scheme is proposed to increase the success rate in automatic sleep stage scoring in which sleep stages were classified as Awake, Non-REM1, Non-REM2, Non-REM3 and REM stages. Using EEG, EMG and EOG recordings of five healthy subjects, a modified version of sequential feature selection method was applied to the sleep epochs in class by class basis and different artificial neural network (ANN) architectures were trained for each class. That is to say, sleep stages were classified with five ANN architectures each of which uses different features and different network parameters for classification. The highest classification accuracy was obtained for REM sleep as 95.13 % in addition to the lowest classification accuracy of 86.42 % for Non-REM3 sleep. The overall accuracy, on the other hand, was recorded as 90.93 %, which is a comparatively good result when the other studies using all stages are taken into account.     

Prediction of power output of a coal-fired power plant by artificial neural network

Accurate modeling of thermal power plant is very useful as well as difficult. Conventional simulation programs based on heat and mass balances represent plant processes with mathematical equations. These are good for understanding the processes but usually complicated and at times limited with large number of parameters needed. On the other hand, artificial neural network (ANN) models could be developed using real plant data, which are already measured and stored. These models are fast in response and easy to be updated with new plant data. Usually, in ANN modeling, energy systems can also be simulated with fewer numbers of parameters compared to mathematical ones. Step-by-step method of the ANN model development of a coal-fired power plant for its base line operation is discussed in this paper. The ultimate objective of the work was to predict power output from a coal-fired plant by using the least number of controllable parameters as inputs. The paper describes two ANN models, one for boiler and one for turbine, which are eventually integrated into a single ANN model representing the real power plant. The two models are connected through main steam properties, which are the predicted parameters from boiler ANN model. Detailed procedure of ANN model development has been discussed along with the expected prediction accuracies and validation of models with real plant data. The interpolation and extrapolation capability of ANN models for the plant has also been studied, and observed results are reported.