Friction materials development by using DOE/RSM and artificial neural network

A group of non-asbestos organic based friction materials containing 16 ingredients were investigated in this work using the techniques of design of experiment (2^k DOE), response surface methodology (RSM), and artificial neural network (ANN). The ingredients effects on three friction characteristics including 1st fading rate, 2nd fading rate, and speed sensitivity were studied by 2^k DOE. Five ingredients of phenolic resin, synthetic graphite, potassium titanate, mineral fiber, and calcium silicate were found to be statistically significant for these responses and should be studied further. In the meantime, an artificial neural network with Elman recurrent configuration was trained and tested using the data generated from dynamometer tests in 2^k DOE experiments. Concerning the confounding of two-ingredient interaction effects and main effects, response surface methodology was employed to optimize the friction material formulation. The well trained and tested Elman artificial neural network was then used to predict the friction characteristics of the trials generated by RSM. Based on the ANN prediction and RSM analysis, an optimization of material formulation was obtained and validated by experiments.     

小波神经网络故障预后模型的建立及其工程应用

故障诊断技术面临两大难题 ,第一如何“测量”故障的发育 ,第二如何预测一个有故障的机器或构件还能正常运行多久。本文用小波基函数神经网络技术解决了这两大课题。首先建立了小波基函数神经网络故障预后模型 ,用高斯基函数和Marr小波函数作为尺度函数 ,基函数中心的计算用二进展开函数和k次聚类函数。诊断实践表明 ,当轴承内表面产生间隙以后 ,应用训练后的小波基函数神经网络能够成功地对其间隙的发育进行预测。     

基于BP神经网络的铣削力仿真技术研究

应用人工神经网络技术建立了铣削力仿真的BP网络模型。通过正交试验,获取训练样本,并对网络进行了训练。最后将网络预测结果与实验数据进行比较和误差分析,证明了人工神经网络能够准确地预测铣削力的大小。     

GENERALISED TRANSFER FUNCTIONS OF NEURAL NETWORKS

When artificial neural networks are used to model non-linear dynamical systems, the system structure which can be extremely useful for analysis and design, is buried within the network architecture. In this paper, explicit expressions for the frequency response or generalised transfer functions of both feedforward and recurrent neural networks are derived in terms of the network weights. The derivation of the algorithm is established on the basis of the Taylor series expansion of the activation functions used in a particular neural network. This leads to a representation which is equivalent to the non-linear recursive polynomial model and enables the derivation of the transfer functions to be based on the harmonic expansion method. By mapping the neural network into the frequency domain information about the structure of the underlying non-linear system can be recovered. Numerical examples are included to demonstrate the application of the new algorithm. These examples show that the frequency response functions appear to be highly sensitive to the network topology and training, and that the time domain properties fail to reveal deficiencies in the trained network structure.     

On-line fan blade damage detection using neural networks

This paper presents a methodology for monitoring the on-line condition of axial-flow fan blades with the use of neural networks. In developing this methodology, the first stage was to utilise neural networks trained on features extracted from on-line blade vibration signals measured on an experimental test structure. Results from a stationary experimental modal analysis of the structure were used for identifying global blade mode shapes and their corresponding frequencies. These in turn were used to assist in identifying vibration-related features suitable for neural network training. The features were extracted from on-line blade vibration and strain signals which were measured using a number of sensors.The second stage in the development of the methodology entails utilising neural networks trained on numerical Frequency Response Function (FRF) features obtained from a Finite Element Model (FEM) of the test structure. Frequency domain features obtained from on-line experimental measurements were used to normalise the numerical FRF features prior to neural network training. Following training, the networks were tested using experimental frequency domain features. This approach makes it unnecessary to damage the structure in order to train the neural networks.The paper shows that it is possible to classify damage for several fan blades by using neural networks with on-line vibration measurements from sensors not necessarily installed on the damaged blades themselves. The significance of this is that it proves the possibility to perform on-line fan blade damage classification using less than one sensor per blade. Even more significant is the demonstration that an on-line damage detection system for a fan can be developed without having to damage the actual structure.     

Solving multiple response optimisation problems using adaptive neural networks

This paper describes an integrated methodology using experimental designs and neural networks technologies for solving multiple response problems. This new methodology consists of an experiment reference template for designing and collecting training data samples and a parallel distributed computational adaptive neural network system to provide a powerful tool for data modelling, guiding experimentation and empirical investigations. While the experiment reference template is for determining the measurements to adopt in order to extract maximum information within minimum experimental efforts, the adaptive neural network provides a nonlinear multivariate data-fitting algorithm for analysing the results of the experimental design and providing decision support. This integrated methodology is used to model and optimise a multiple response metal inert gas (MIG) welding process. The neural network is trained with optimum welding experimental data, tested and compared in an actual welding environment in terms of weld quality. The relevant data is established using experimental design methods and is highlighted in the case study. The implementation for this case study was carried out using a semi-automatic welding facility, to mass weld a 20 in.×0.438 in. pin/box onto a 20 in.×0.5 in.×37 ft pipe (tubular drilling products), in an actual workshop which makes oilfield equipment. The entire range of welding combination that the process might be subject to during actual welding operations is included to study the weld quality.     

Optimization of shape rolling sequences by integrated artificial intelligent techniques

The present work introduces an expert system that automatically selects and designs rolling sequences for the production of square and round wires. The design strategy is aimed at reducing the overall number of passes assuming a series of process constraints, e.g., available roll cage power and torque, rolls groove filling behaviors, etc. The method is carried out into two steps: first a genetic algorithm is used to select the proper rolling sequence allowing to achieve a desired finished product; then, an optimization roll pass design tool is utilized for proper design of roll passes. Indeed, an artificial neural network (ANN) is utilized to predict the main geometrical characteristics of the rolled semi-finished product and technological requirements. The ANN was trained with a non-linear finite element (FE) model. The proposed methodology was applied to some industrial cases to show the validity of the proposed approach in terms of reduction of number of passes and search robustness.     

Parameter prediction in laser bending of aluminum alloy sheet

Based on the basic platform of BP neural networks, a BP network model is established to predict the bending angle in the laser bending process of an aluminum alloy sheet (1–2 mm in thickness) and to optimize laser bending parameters for bending control. The sample experimental data is used to train the BP network. The nonlinear regularities of sample data are fitted through the trained BP network; the predicted results include laser bending angles and parameters. Experimental results indicate that the prediction allowance is controlled less than 5%–8% and can provide a theoretical and experimental basis for industry purpose.     

Neural networks approach to the determination of the machining parameters

A neural networks based approach to determine the appropriate machining parameters such as speed, depth of cut and feed is proposed in this study. In this approach neural networks were used for building automatic process planning systems. Training of neural networks was performed with back propagation method by using data sets sampled in a standard handbook. These networks consist of simple processing, elements or nodes capable of processing information in response to external inputs. This approach saves computing time and storage space. In addition, it provides easy extendability as new data become available. Currently, the system provides three neural networks: for turning, for milling and for drilling operations. The performance of the trained neural network for drilling is evaluated to examine how well it predicts the machining parameters. Test results show that the neural network for the turning operation is able to predict the machining parameter values within an acceptable error rate.     

Using neural networks for the diagnosis of localized defects in ball bearings

Two neural network based approaches, a multilayered feed forward neural network trained with supervised Error Back Propagation technique and an unsupervised Adaptive Resonance Theory-2 (ART2) based neural network were used for automatic detection/diagnosis of localized defects in ball bearings. Vibration acceleration signals were collected from a normal bearing and two different defective bearings under various load and speed conditions. The signals were processed to obtain various statistical parameters, which are good indicators of bearing condition, and these inputs were used to train the neural network and the output represented the ball bearing states. The trained neural networks were used for the recognition of ball bearing states. The results showed that the trained neural networks were able to distinguish a normal bearing from defective bearings with 100% reliability. Moreover, the networks were able to classify the ball bearings into different states with success rates better than those achieved with the best among the state-of-the-art techniques.     

基于EBI和C-Bus技术的楼宇空调自动化监控系统

根据洁净区间的空气处理系统要求和空调系统结构特点,采用工业自动化数字控制系统,实现对智能楼宇空调系统的自动化管理。介绍了EBI和C-Bus总线的空调自动化监控系统网络结构、空调系统组成及功能、现场控制网络节点硬件构成和DDC软件编程、EBI监控平台的组态设计。经工程项目调试,系统能满足洁净区间空调系统控制要求,现已投入使用,运行效果良好,达到了设计指标要求,具有良好的应用价值。     

Artificial neural networks for predicting sliding friction and wear properties of polyphenylene sulfide composites

In this paper the potential of using artificial neural networks (ANNs) for the prediction of sliding friction and wear properties of polymer composites was explored using a newly measured dataset of 124 independent pin-on-disk sliding wear tests of polyphenylene sulfide (PPS) matrix composites. The ANN prediction profiles for the characteristic tribological properties exhibited very good agreement with the measured results demonstrating that a well trained network had been created. The data from an independent validation test series indicated that the trained neural network possessed enough generalization capability to predict input data that were different from the original training dataset.     

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.     

Multi-machine earliness and tardiness scheduling problem: an interconnected neural network approach

This paper addresses the problem of scheduling a set of independent jobs with sequence-dependent setups and distinct due dates on non-uniform multi-machines to minimize the total weighted earliness and tardiness, and explores the use of artificial neural networks as a valid alternative to the traditional scheduling approaches. The objective is to propose a dynamical gradient neural network, which employs a penalty function approach with time varying coefficients for the solution of the problem which is known to be NP-hard. After the appropriate energy function was constructed, the dynamics are defined by steepest gradient descent on the energy function. The proposed neural network system is composed of two maximum neural networks, three piecewise linear and one log-sigmoid network all of which interact with each other. The motivation for using maximum networks is to reduce the network complexity and to obtain a simplified energy function. To overcome the tradeoff problem encountered in using the penalty function approach, a time varying penalty coefficient methodology is proposed to be used during simulation experiments. Simulation results of the proposed approach on a scheduling problem indicate that the proposed coupled network yields an optimal solution which makes it attractive for applications of larger sized problems.     

Determining the parameters of dual-card kanban system: an integrated multicriteria and artificial neural network methodology

In this study, we proposed a methodology for determining the design parameters of kanban systems. In this methodology, a backpropagation neural network is used in order to generate simulation meta-models, and a multi-criteria decision making technique (TOPSIS) is employed to evaluate kanban combinations. In order to reflect the decision maker’s point of view, different weight structures are used to find the optimum design parameters. The proposed methodology is applied to a case problem and the results are presented. We also performed several experiments on different types of problems to show the effectiveness of the methodology.     

一种基于3G网络的分布式远程无线测控系统的设计

3G网络是新兴的无线公共的移动传输网络,具有传输速度高、覆盖范围广的特点.该文提出一种基于3G网络的远程无线测控系统的设计方案,利用3G路由器和串口上网模块将远程的现场测控终端连接至INTERNET中具有静态IP的上位机,通过TCP协议实现上位机与测控终端的无线、高速、透明数据传输.介绍系统的2个关键设备:3G路由器和...     

重载大跨距横梁弯曲变形分析与补偿

针对重载大跨距横梁的弯曲变形问题,将有限元数值计算和BP神经网络相结合,提出横梁弯曲变形预测方法,通过预制补偿曲线辅助进行横梁弯曲补偿,提高横梁几何精度。首先,利用ANSYS分析软件获得溜板位于横梁一系列工作位置的变形量,作为神经网络的训练样本;其次,通过在Matlab中编程调整网络参数,建立了满足误差要求的BP神经网络模型,并进行训练,利用训练后的神经网络预测横梁变形曲形;最后,对预制补偿曲线的横梁进行弯曲变形测量,实验表明神经网络预测值与实验数据较吻合,相对误差<15%,并且运行时间只需0.27 s。研究结果表明,该方法能够较为准确地预测横梁弯曲变形并进行补偿,为重载大跨距横梁结构设计与预制补偿曲线提供了新的思路和技术支持。     

基于粗糙集神经网络的产品族配置性能预测方法

模块化产品族设计是面向大规模定制设计的支撑技术.为了加快对动态变化的个性化需求的响应速度,提出基于粗糙集神经网络的产品族配置性能预测新方法,以通过产品族中典型产品变型的历史数据挖掘来预测新产品变型的基本性能,给出产品族配置性能预测定义和目标;提出层次化的产品族表示模型,并用数学方法对配置过程和配置对象进行规范描述;给出基于粗糙集神经网络的配置性能综合预测框架和基本步骤.该方法能够复用所挖掘的配置知识和配置规则,减少试验环节工作量,且能将性能预测值作为衡量是否满足最终客户需求的基本依据,以评价配置的合理性.最后以某模块化新型号冰箱产品族为实例进行验证.     

基于粒子群优化的神经网络训练算法在产品种类预测中的应用

市场环境的变化导致产品更新换代加快，产品种类预测成为新的难题。传统的线性预测方法只能对产品需求的数量或价格等数值进行预测，而无法对产品的发展趋势和未来种类做出正确预测。通过对产品种类预测、数据挖掘和粒子群优化算法的研究，建立种类预测模型，利用基于粒子群优化的神经网络训练算法进行产品种类预测，并以手机为例进行预测，结果证明该方法是有效的。     

基于遗传神经网络的压铸机座板结构优化设计

在对压铸机合模机构进行结构设计时,利用神经网络的非线性映射能力,通过少量样本的有限元分析结果,训练出表述结构参数间函数关系的神经网络模型,然后利用遗传算法的全局寻优性找到神经网络模型表述的目标函数的最优结构参数,从而解决结构优化设计的瓶颈和智能问题,利用这种优化设计策略,设计了压铸机合模机构座板,结果表明了该方法的高效性。