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.     

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.     

人工神经网络在智能机械设计中的应用

介绍了智能ＣＡＤ的概念和发展,分析了人工神经网络（ＡＮＮ）的特点,针对目前机械设计专家系统存在的问题,提出将ＡＮＮ应用到专家系统的设计中,是进行智能ＣＡＤ的一条有效途径。介绍了ＡＮＮ在概念设计、设计过程中形象思维的模拟、知识的获取和表示、回溯问题的模拟等方面的应用。     

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.     

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.     

人工神经网络技术在CBN砂轮磨削表面粗糙度研究中的应用

针对CBN砂轮磨削 ,采用人工神经网络方法建立由磨削用量确定表面粗糙度的预测模型。计算结果证明 ,所建立的人工神经网络模型可很好地描述砂轮速度、砂轮进给速度、工件转速对磨削表面粗糙度的影响。预测结果具有良好的精度并得到了验证试验的检验。通过本模型 ,利用有限的试验数据可得出整个工作范围内表面粗糙度的预测值 ,可大量减少试验费用     

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.     

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.     

Indirect model for roughness in rough honing processes based on artificial neural networks

In the present paper an indirect model based on neural networks is presented for modelling the rough honing process. It allows obtaining values to be set for different process variables (linear speed, tangential speed, pressure of abrasive stones, grain size of abrasive and density of abrasive) as a function of required average roughness Ra. A multilayer perceptron (feedforward) with a backpropagation (BP) training system was used for defining neural networks. Several configurations were tested with different number of layers, number of neurons and type of transfer function. Best configuration for the network was searched by means of two different methods, trial and error and Taguchi design of experiments (DOE). Once best configuration was found, a network was defined by means of trial and error method for roughness parameters related to Abbott–Firestone curve, Rk, Rpk and Rvk.     

Automatic recognition of machining features from a solid model using the 2D feature pattern

Feature recognition systems are now widely identified as a cornerstone for conceiving an automated process planning system. Various techniques have been reported in the literature, but a few of them acquired a status of generic methodology. A flexible and robust approach is demanded for recognising a wide variety of features, e.g., non-interacting, interacting circular and slanting features. This research aims to exploit the concept of the ray - firing technique, in which a 2D surface pattern for each feature is generated and information is extracted from these patterns to correlate it with the corresponding machining features. The system first defines a virtual surface and then probing rays are dropped from each point of this surface to the 2.5D features of the B-rep solid model. According to the length of rays between the bottom face of the 2.5D machining features and the virtual surface, 2D feature patterns are formed for each machining feature. Finally, features are recognised using an algorithm described in this article. Different types of examples have been considered to demonstrate the effectiveness of the proposed approach.     

Prediction of biodegradability of mineral base oils from chemical composition using artificial neural networks

Biodegradability studies of base oils are important for designing and development of environment-friendly lubricants. Biodegradability of base oils and lubricants have been determined by a large number of test methods. Among these, the 21 day test developed by Coordinating European Council and designated as CEC-L-33-A-93 has been accepted worldwide. In this work, artificial neural network (ANN) technique has been used to construct mathematical models for predicting biodegradability of base oils based upon their chemical composition, viscosity and viscosity index. The chemical composition has been determined either by NMR or mass spectrometry and two models have been developed. Thirty-one base oils of different origin and processing schemes, and their blends with polyalphaolefin (PAO) were analyzed for chemical composition and biodegradability. Part of the data was used for developing the models using backpropagation ANN, while the remaining data were used for evaluating the predictive ability of the models (correlation coefficient, R?²∼0.97). The models can serve as useful tools for screening base oils before subjecting them to 21 day biodegradability test.     

New correlations for better monitoring the all-oil mud rheology by employing artificial neural networks

The rheological properties of the drilling fluid are crucial to the success of the drilling project. The traditional mud experiments normally performed by the mud engineers provide rheological data with a small resolution. Monitoring higher-resolution rheological properties is particularly important for all-oil mud because it is widely used with problematic drilled formations. The design and monitoring of the drilling fluid rheology is a critical issue for drilling, and therefore, this paper is a contribution to the effort to completely automate the process of highly accurate and real-time recording of the rheological mud properties. This paper aims to develop intelligent predictive models for the mud rheological properties using artificial neural networks [ANN] by linking the high-frequency mud parameters such as fluid density or mud weight [MWT] and Marsh funnel viscosity [MFV] with the rheological measurements of low frequency for drilling mud such as plastic viscosity [PV], yield point [YP], behavior indicator [n] and viscosity appearance [AV]. New empirical correlations have additionally been established to assess the rheological properties of water. In order to construct ANN models, data was obtained from 56 different wells during drilling operations of different drilling sections with various sizes. The data was fairly enough for building and testing the models as 369 data points were obtained. The models were optimized by trainlm which was the best training function and tansig was the best transfer function. 42 neurons in the hidden layer optimized AV and PV models where 34 neurons optimized all other rheological models [YP, n, R300, and R600]. ANN models presented good results as correlation coefficient [R] was 0.9 and an average absolute [AAPE] error of less than 8% for training and testing data sets. The new models were used to derive the empirical correlations for the estimation of rheological parameters. The empirical correlations were extracted to easily monitor the rheological properties of an all-oil mud system in real-time, which enables better control of the drilling activity by maintaining rheological properties at optimal values as well as early detection of other problems that might require immediate interactions, including well control and stuck pipe.     

Application of artificial neural networks for prediction of coercivity of highly ordered cobalt nanowires synthesized by pulse electrodeposition

This study aims to predict the coercivity of cobalt nanowires fabricated by Alternating Current (AC) pulse. Coercivity is one of the most important properties of magnetic materials and its value shows the needed magnetic field in a way that magnetization of system is decreased to zero. There are many parameters such as pH of solution, oxidative and reductive times, oxidative and reductive voltages, interval between pulses (off-time), and concentration of deposition solution that have direct effect on materials magnetic properties of. Change of initial conditions to obtain the best results is very time consuming, therefore employing a method which can save both the time and cost is necessary. Hence, it this study Artificial Neural Network (ANN), which has numerous applications and has attracted many attentions in various fields, was applied. Through this study, an ANN was designed to present a template that is capable for predicting output data (coercivity) according to input data (pH, oxidative and reductive times, oxidative and reductive voltages, and off-time). Besides, in this research, the results for pH = 4 and 6 were investigated and the effect of off-time as well as the deposition time on coercivity were studied.     

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.     

Diagnosis of roller bearing defects using neural networks

A new approach for the diagnosis of bearing defects has been utilised. Artificial neural networks (ANN) were employed for the diagnosis of various kinds of bearing defects. The features selected for this purpose were: the average of the top five values of amplitude in the high-frequency region (5 kHz-22 kHz), the peak value of the amplitude in the high-frequency region, the average of the top five values in the prime spike region (340 Hz-3262 Hz), the autocorrelation function in the prime spike region, the autocorrelation function in the high-frequency region, and the cepstrum function in the high-frequency region.Data were collected using a data acquisition system. The data collected for the five different defective roller bearings as well as for a normal bearing were used to train neural networks. The trained neural networks were used for the diagnosis of roller bearings. Various neural network sizes were used. It was found that neural networks were able to distinguish a normal bearing from defective bearings with 100% reliability. Furthermore, roller bearings can be classified into six different states with a success rate of up to 94%.     

Analysis of hydraulic characteristics for hollow semi-circular weirs using artificial neural networks

Weirs are small overflow dams used to alter and raise water flow upstream and regulate or spill water downstream watercourses and rivers. This paper presents the application of artificial neural network (ANN) to determine the discharge coefficient (Cd) for a hollow semi-circular crested weirs. Eighty five experiments were performed in a horizontal rectangular channel of 10 m length, 0.3 m width and 0.45 m depth for a wide range of discharge. The results of examination for discharge coefficient were yielded by using multiple regression equation based on dimensional analysis. Then, the results obtained were also compared using ANN techniques. A multilayer perceptron MLP algorithm FFBP network was developed. The optimal configuration of ANN was [2,10,1] which gave mean square error (MSE) and correlation coefficient (R) of 0.0011 and 0.91, respectively. Performances of ANN model reveal that the Cd could be better estimated by the ANN technique in comparison with Cd obtained using statistical approach.     

Geometric Feature Recognition for Reverse Engineering using Neural Networks

1. Point data reduction module. 2. Edge detection module. 3. ANN-based feature recogniser. 4. Feature extraction modules. This approach was validated with a variety of real industrial components. The test results show that the developed feature-based RE application proved to be suitable for reconstructing prismatic features such as blocks, pockets, steps, slots, holes, and bosses, which are very common in mechanical engineering products. An example is presented to validate this approach.     

人工神经网络在超声振动-放电复合加工模糊控制中的应用

针对模糊控制系统,构建了相应的人工神经网络,利用matlab6.5平台,对人工神经网络进行了训练,得出了神经网络训练后的的权重和阈值,并对系统进行了仿真。     

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.     

Prediction of friction coefficient of treated betelnut fibre reinforced polyester (T-BFRP) composite using artificial neural networks

The current work is an attempt of using artificial neural network configuration to predict frictional performance of treated betelnut fibre reinforced polyester (T-BFRP) composite. Experimental dataset at different applied loads (5-30 N) and sliding distances (0-6.72 km) was used to train the ANN configuration with a large volume of experimental data (492 sets) where three different fibre mat orientations were considered (anti parallel, parallel and normal orientations). Results obtained from the developed ANN model were compared with experimental results. It is found that the experimental and numerical results showed good accuracy when the developed ANN model was trained with Levenberg-Marqurdt training function.