Flank wear regulation using artificial neural networks

Tool wear regulation highly influences product quality and the safety and productivity of machining processes. Hence, it is one of the most important elements in the supervisory control of machine tools. The development of this type of machine tool adaptive control is practically at its infancy because there are still no industrial solutions concerning robust, reliable, and highly precise continuous tool wear estimators. Therefore, this paper primarily aims at the determination of a tool wear regulation model that can ensure the maximum allowed amount of tool wear rate within a predefined machining time, while simultaneously maintaining a high level of process productivity. The proposed model is structured using Radial Basis Function Neural Network controller and Modified Dynamical Neural Network filter. It is analysed using an analytical tool wear model with experimentally adjusted parameters.     

Dimensional and angular measurements using least squares and neural networks

In this study a machine vision approach is developed for dimensional and angular measurements of manufactured components comprising straight line segments. We aim at the measurements of distance between two parallel lines and angle between two intersecting lines using both least mean square (LMS) and artificial neural network (ANN) techniques. LMS models estimate the line parameters based on the sum of squared perpendicular distances, rather than the vertical distances, between the observed data points and the line. A set of 23 gauge blocks of varying sizes is used to evaluate the performance of the LMS line estimators. Experimental results show that the measurement errors of the LMS models are affected by the line length and orientation of digital images. ANN techniques are, therefore, used to adjust the measurement errors resulting from the LMS models. Two back-propagation neural networks are developed, one for measuring the distance between two parallel lines, and the other for measuring the angle between two intersecting lines. Experimental results show that the ANNs are very effective for correcting the measurement errors regardless of line lengths and orientations of digital images. A 90% improvement in measurement accuracy for the ANN compared to the LMS was achieved. By using the ANNs, the measurement accuracy and flexibility in manufacturing applications can be significantly improved.     

Modeling of an intelligent pressure sensor using functional link artificial neural networks

A capacitor pressure sensor (CPS) is modeled for accurate readout of applied pressure using a novel artificial neural network (ANN). The proposed functional link ANN (FLANN) is a computationally efficient nonlinear network and is capable of complex nonlinear mapping between its input and output pattern space. The nonlinearity is introduced into the FLANN by passing the input pattern through a functional expansion unit. Three different polynomials such as, Chebyschev, Legendre and power series have been employed in the FLANN. The FLANN offers computational advantage over a multilayer perceptron (MLP) for similar performance in modeling of the CPS. The prime aim of the present paper is to develop an intelligent model of the CPS involving less computational complexity, so that its implementation can be economical and robust. It is shown that, over a wide temperature variation ranging from -50 to 150 degrees C, the maximum error of estimation of pressure remains within +/- 3%. With the help of computer simulation, the performance of the three types of FLANN models has been compared to that of an MLP based model.     

A smart force platform using artificial neural networks

The human body may interact with structures and these interactions are developed through the application of contact forces, for instance when walking. The aim of this paper is to propose a new methodology using Artificial Neural Network (ANN) for calibrating a force platform in order to reduce the uncertainties in the values of estimated vertical Ground Reaction Force and the positioning of the applied force in the human gait. Force platforms have been used to evaluate the pattern of human applied forces and to fit models for the interaction between pedestrians and structures. Linear relation assumptions between input and output are common in traditional Least Mean Square methods used in calibration. Some discrepancies due to nonlinearities in the experimental setup (looseness, wear, support settlements, electromagnetic noise, etc.) may harm the overall fitting. Literature has shown that nonlinear models, like ANN, can better handle this. During the calibration, the input data to the ANN were the reference voltages applied to the Wheatstone bridge, while the output data were the values of the standard weights applied in the force platform in defined sites. Supervised training based on k-fold cross validation was used to check the ANN generalization. The use of ANN shows significant improvements for the measured variables, leading to better results for predicted values with low uncertainty when compared to the results of a simple traditional calibration using Least Mean Squares.     

基于移动机器人的多超声波传感器信息融合

智能化是未来机动机器人的发展方向,而智能化的基础是传感器技术的发展,机器人信息融合技术则弥补了使用单传感器带来的缺陷。本文利用多超声波传感器系统荻得移动机器人的环境信息,并使用人工神经网络对多传感器信息进行融合,从而达到对被测物体影像比较准确的认识,试验表明效果良好,具有很好的使用价值。     

Automatic recognition of machining features using artificial neural networks

We report on the development of an intelligent system for recognizing prismatic part machining features from CAD models using an artificial neural network. A unique 12-node vector scheme has been proposed to represent machining feature families having variations in topology and geometry. The B-Rep CAD model in ACIS format is preprocessed to generate the feature representation vectors, which are then fed to the neural network for classification. The ANN-based feature-recognition (FR) system was trained with a large set of feature patterns and optimized for its performance. The system was able to efficiently recognize a wide range of complex machining features allowing variations in feature topology and geometry. The data of the recognized features was post-processed and linked to a feature-based CAPP system for CNC machining. The FR system provided seamless integration from CAD model to CNC programming.     

利用混沌人工神经元网络进行布局优化计算

将混沌人工神经元网络引用到布局的优化求解计算中。讨论了混沌人工神经元网络的模型 ,以及布局问题到混沌神经网络的映射 ,并结合 2维规则物体的布局问题通过软件模拟计算对所讨论的算法进行了验证。     

Identifying three-phase induction motor faults using artificial neural networks

This paper presents an artificial neural network (ANN) based technique to identify faults in a three-phase induction motor. The main types of faults considered are overload, single phasing, unbalanced supply voltage, locked rotor, ground fault, over-voltage and under-voltage. Three-phase currents and voltages from the induction motor are used in the proposed approach. A feedforward layered neural network structure is used. The network is trained using the backpropagation algorithm. The trained network is tested with simulated fault current and voltage data. Fault detection is attempted in the no fault to fault transition period. Off-line testing results on a 3 HP induction motor model show that the proposed ANN based method is effective in identifying various types of faults.     

Due-date assignment in wafer fabrication using artificial neural networks

Due-date assignment (DDA) is the first important task of shop floor control in wafer fabrication. Due-date related performance is impacted by the quality of the DDA rules. Assigning order due dates and timely delivering the goods to the customer will enhance customer service and competitive advantage. A new methodology for lead-time prediction, artificial neural network (ANN) prediction is considered in this work. An ANN-based DDA rule combined with simulation technology and statistical analysis is developed. Besides, regression-based DDA rules for wafer fabrication are modelled as benchmarking. Whether neural networks can outperform conventional and regression-based DDA rules taken from the literature is examined. From the simulation and statistical results, ANN-based DDA rules perform a better job in due-date prediction. ANN-based DDA rules have a lower tardiness rate than the other rules. ANN-based DDA rules have better sensitivity and variance than the other rules. Therefore, if the wafer fab information is not difficult to obtain, the ANN-based DDA rule can perform better due-date prediction. The SFM_sep and JIQ in regression-based and conventional rules are better than the others.     

Time domain modeling of cup anemometers using artificial neural networks

Under rapidly fluctuating wind speed, high inertia cup anemometers have a tendency to overspeed. The main objective of this article is to develop an inverse time domain model that could be used in real-time during cup anemometer operation to minimize the so-called “u-error”. A model proposed by Kristensen and an artificial neural network (ANN) direct model were first investigated to simulate the dynamic behavior of a heated cup anemometer with relatively high rotor inertia. Once the anemometer behavior was known, several virtual inputs were generated and the direct model was used to predict the instrument behavior. These models were built to emphasis the non-linear relationship between the free stream fluctuating wind and the wind speed measured by the anemometer. A semi-empirical inverse model derived from Kristensen's model was then studied and an ANN inverse model was suggested in order to minimize the so-called u-error. A methodology is proposed to gather the appropriate data to create both the direct and inverse model using an artificial neural network. The output of each model was compared with experimental data for validation and good agreement was found between the ANN models and the experimental data used for validation.     

Improvement of the five-hole probe calibration using artificial neural networks

In the present study, the artificial neural networks (ANNs) technique was implemented to link non-dimensional pressure coefficients and flow characteristics to calibrate a five-hole probe. The experimental data of this work were obtained from a subsonic open-circuit wind tunnel at the velocity of 10 m/s. Here, the efficiency of ANNs was compared with two conventional data reduction methods, including linear interpolation technique and 5^th-order polynomial surface fit algorithm. Based on the statistical parameters of calibration data, it was concluded that the radial basis function (RBF) algorithm was more accurate and had more flexibility compared to the multi-layer perceptron (MLP) regression algorithm, the linear interpolation and 5^th-order polynomial methods. In the RBF method, the mean absolute errors of 0.11, 0.64, 0.02 and 0.03 were achieved for α, β, Cp_t and Cp_s , respectively. Furthermore, the effects of training data reduction and data selection on the performance of RBF were studied. The accuracy of the proposed RBF method was analyzed at different α angles and for random test data. Finally, the influence of increasing number of test data on the efficiency of calculated RBF method was evaluated.     

应用BP网络和遗传算法的快速分析与优化

将BP神经网络和遗传算法相结合运用于快速设计的结构优化问题。利用已有的实例对神经网络进行训练达到要求精度,使神经网络能够代替有限元分析达到快速分析的目的。用神经网络计算遗传算法的适应度,对结构进行优化。把BP神经网络和遗传算法相结合进行分析和优化符合快速设计的思想。此方法运用于某拱梁的快速优化设计中效果良好。     

Improved recognition of control chart patterns using artificial neural networks

Recognition of abnormal patterns in control charts provides clues to reveal potential quality problems in the manufacturing processes. One potentially popular approach for recognizing different control chart patterns (CCPs) is to develop heuristics based on various shape features of the patterns. The advantage of this approach is that the users can easily understand how a particular pattern is identified. However, consistency in the recognition performance is found to be considerably poor in the heuristics approach. Since shape features represent the main characteristics of the patterns in a condensed form, artificial neural network (ANN) with features extracted from the process data as input vector representation can facilitate efficient pattern recognition with a smaller network size. In this paper, a set of seven shape features is selected, whose magnitudes are independent of the process mean and standard deviation under a special representation of the sampling interval in the control chart plot. Based on these features, the CCPs are recognized using a multilayered perceptron neural network trained by back-propagation algorithm. The recognizer can recognize all the eight commonly observed CCPs. Extensive performance evaluation of this recognizer is carried out using simulated pattern data. Numerical results indicate that the developed ANN recognizer can perform well in real time process control applications with respect to both recognition accuracy and consistency.     

Process modelling and optimisation using artificial neural networks and gradient search method

Process modelling refers to the development of a process model that serves to provide the input-output relationship of a process, while process optimisation provides the optimum operating conditions of a process for a high-yield, low cost and robust operation. Normally, process modelling is a starting point of process optimisation. In this paper, a method of integrating artificial neural networks with a gradient search method for process modelling and optimisation is presented. Artificial neural networks are used to develop process models while a gradient search method is used in process optimisation. Application of the method to the modelling and optimisation of epoxy dispensing for microchip encapsulation is described. Results of the validation tests indicate that good quality of encapsulation can be obtained based on the proposed method.     

Prediction of brake friction materials recovery performance using artificial neural networks

The brake friction materials in an automotive brake system are considered as one of the key components for overall braking performance of a vehicle. The sensitivity of friction material performance and accordingly brake performance, versus different operating regimes, has always been an important aspect of its functioning. In this paper, the influences not only on the brake operation conditions but also on the formulation and manufacturing conditions of friction materials have been investigated regarding friction materials recovery performance by means of artificial neural networks. A new neural network model of friction material recovery performance, trained by the Bayesian Regulation algorithm, has been developed.     

Retrieval of machining information from feature patterns using artificial neural networks

The integration of design and manufacturing has been the subject of much debate and discussion over a long period of time. Recognition of feature patterns and the retrieval of necessary machining information from those patterns play vital roles in this process of integration, as they facilitate the selection of the necessary manufacturing parameters required to transform the designed product into a final physical entity. Although the problem of recognising features from a solid model has been exclusively studied, most existing product models are expressed as engineering drawings. Moreover, the solid model can only provide complete 3D topological and geometrical data and some of the essential machining information cannot be retrieved. In this paper, an approach for defining engineering features, like slots, steps and circular pockets is proposed using binary strings. Two artificial neural networks, one for slots and steps and the other for circular pockets, are designed and developed. These neural networks take the binary strings as inputs and give the relevant machining information as outputs. The networks are trained with non-interacting features and after training, those will become capable of providing the necessary machining information for both non-interacting and interacting features in the domains of slots, steps and circular pockets. This novel approach can further be extended to other features for retrieving relevant machining information and thus facilitating the effective integration of design and manufacturing.     

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.     

Feedrate optimization based on hybrid forward-reverse mappings of artificial neural networks for five-axis milling

With recent advances in five-axis milling technology, feedrate optimization methods have shown significant effects in regard to enhancing milling productivity, especially when machining complex surface parts. The existing study is aimed at calculating the optimal feedrate values through modeling milling processes. However, due to the complexity of five-axis milling processes, optimization efficiency is the bottleneck of applying them in practice. This paper proposes a novel milling process optimization method based on hybrid forward-reverse mappings (HFRM) of artificial neural networks. The feedrate values are directly used as the outputs of network mappings. Three kinds of artificial neural networks are compared to determine the one with the highest accuracy and the best training efficiency. The study shows that with the collected datasets, the trained Levenberg-Marquardt back-propagation network (LMBPN) could predict feedrate values more precisely than other alternatives. Compared with previous methods, this HFRM-based optimization method is more adept in the area of parameter adjustment because as it has the advantages of high precision and much less calculation time. Combining other multiple milling constraints, an optimization system is developed for five-axis milling processes. The optimized results could be directly used to modify a cutter location (CL) file. A typical milling case was provided to verify the optimization performance of this method, which was found to be effective and reliable.     

Modeling the cleanliness level of an ultrasonic cleaning system by using design of experiments and artificial neural networks

The hard disk drive is a reliable and relatively cheap mass storage device used in every computer nowadays. In this study, one major issue affecting the product quality of the fixture inside a hard disk drive is the surface contamination of the arm finger of actuator (AFA). For economical exploitation, a primary concern is to generate a model for optimizing the process parameter settings necessary to sustain the desired cleanliness level in an ultrasonic cleaning process. Two approaches were employed to identify critical process parameters, followed by the determination of the optimal parameter settings. The former approach was a statistical design of experiments (DOE) for developing regression equations for predicting the cleanliness level and finding out the dependence of each parameter and outcome. The latter approach was in using an artificial neural network (ANN) for building prediction models. A comparative study showed that both approaches have advantages over other methods. The results obtained show a reduction in contamination of the AFA; hence it provides an aid in the improvement of product quality.