Sequential approximate robust design optimization using radial basis function network

This paper proposes a sequential approximate robust design optimization (SARDO) with the radial basis function (RBF) network. In RDO, the mean and the standard deviation of objective should be minimized simultaneously. Therefore, the RDO is generally formulated as bi-objective design optimization. Our goal is to find a robust optimal solution with a small number of function evaluations, not identifying a set of Pareto-optimal solution using Multi-Objective Evolutionary Algorithms. The weighted sum is often used to find a robust optimal solution. In contrast, the weighted lp norm method is used in this paper. Through illustrative examples, some validations of the weighted lp norm method to the RDO are clarified. Next, SARDO with the RBF network is discussed. In general, the standard deviation of functions is obtained by using the finite difference method. Thus, in order to obtain the standard deviation of functions, the finite difference method is directly applied to the response surface. High accuracy of the finite difference method will leads to highly accurate robust optimal solution. In order to avoid the inaccurate numerical calculation, the standard deviation is expressed by only the Gaussian kernel. As the result, it is expected that a highly accurate robust optimal solution can be found with a small number of function evaluations. Through numerical examples, the validity of the proposed approach is examined. Finally, the variable blank holder force trajectory for reducing springback is examined.     

基于CAD/CAM的复杂曲面数控三坐标投影加工误差分析

两刀位点间连线与原有刀具轨迹所在平面的夹角α(或曲面上刀位数据的Z坐标增量Δz)对基于CAD/CAM的复杂曲面数控三坐标投影加工误差十分敏感.当用弦长代替弧长时,投影加工两刀位点间直线插补轨迹的法向加工误差Δt是以原有刀具轨迹走刀步长或行距加工曲面时两刀位点间直线插补轨迹的法向加工误差Δy的cos2α(0°<α<90°)倍.若α(或Δz)达到一定值,就会因原有走刀步长或行距的过渡拉伸而产生严重的过切(步长拉伸时)或欠切(行距拉伸时)现象,使被加工曲面出现明显的折面或残留面.应引入控制参数α(或Δz),根据曲面加工精度要求设定限值αmax(或Δzmax),当α>αmax(或Δz>Δzmax)时,进行刀位密化处理,以保证曲面加工精度.     

基于径向基函数神经网络的CFRP切削力预测

碳纤维增强树脂基复合材料(CFRP)加工中基体相极易因切削力过大而破坏,并迅速扩展至加工表面以下而形成损伤。为了准确预测其切削力并加以控制,基于实验切削力数据建立了人工神经网络切削力模型,预测了不同纤维角度、切削深度和刀具角度下加工CFRP的切削力变化规律,并完成了不同刀具角度及切削参数下典型纤维角度CFRP单向板的直角切削实验,对预测模型进行验证,其预测精度可达85%以上。结合成屑过程在线显微观测结果可知:纤维角度是影响CFRP切削力的主要因素, 0°~135°范围内,切屑形成方式为切断型和开裂后弯断型;切削力随纤维角度增大呈先减小后增大的趋势, 135°时最大,随切削深度增加,切削力总体呈增大趋势。      

基于特征的CAD／CAM集成模型的研究

基于特征建模的CAD／CAM集成技术的研究已经成为CAD发展的一个重要方向，也是实现CIMS必须要解决的关键技术之一，根据在I－DEAS基础上对特征建模技术的研究，重点介绍了有关特征描述，特征定位及特征组合等方面的问题，并介绍了特征模型的管理方法及发展前景。     

Geometrical error compensation of precision motion systems usingradial basis function

This paper describes a new method for geometrical error compensation of precision motion systems. The compensation is carried out with respect to an overall geometrical error model which is constructed from the individual error components associated with each axis of the machine. These error components are modeled using radial basis functions (RBFs), thus dispensing with the conventional look-up table. The adequacy and clear benefits of the proposed approach are illustrated from an application to an XY table     

复杂曲面整体叶轮CAD/CAM技术研究

根据整体叶轮的结构特点和工艺要求，提出了复杂曲面整体叶轮CAD/CAM系统的结构，探讨了各模块的主要功能。对开发过程中的关键问题：直纹面叶片曲面的加工及无干涉刀位轨迹生成算法进行了讨论，并给出了相应的仿真。     

Options valuation by using radial basis function approximation

This paper describes the valuation scheme of European, barrier, and Asian options of single asset by using radial basis function approximation. The option prices are governed with Black–Scholes equation. The equation is discretized with Crank–Nicolson scheme and then, the option price is approximated with the radial basis functions with unknown parameters. In the European and the barrier options, the prices are governed with Black–Scholes equation. The governing option of the Asian option, however, is different from them of the others. In that case, one has to adopt the other radial basis functions than that for the original Black–Scholes equation.Finally, numerical results are compared with theoretical and finite difference solutions in order to confirm the validity of the present formulation.     

CAD/CAM在连杆电极设计和加工中的应用

Cimatron软件以其简单易用的统一界面为设计者从产品结构设计、零件设计,到模具设计、电极设计和加工提供了一个完整的解决方案.用Cimatron软件设计连杆模型,并制作出电极,很好地保证了产品的尺寸、重量和外观要求,而且工艺调整时,能迅速地修改电极模型,方便快捷,既能保证工艺要求,又大大缩短了产品开发周期.     

基于Pro/TOOLKIT的包装容器CAD/CAM系统研究

以Pro/Engineer为二次开发平台,利用其二次开发工具Pro/TOOLKIT,在Visual C 6.0编程平台上,开发出了一个塑料瓶及其吹塑模具的三维参数化CAD/CAM系统,提供了一种开发包装CAD/CAM软件的方法.     

Analysis of anisotropic plate using multiquadric radial basis function

In this paper, multiquadric radial basis function is used for dynamic and static analysis of anisotropic plates. Multiquadric radial basis function is applied for spatial discretization and Newmark implicit scheme is used for temporal discretization. The spatial discretization of the differential equations generates greater number of algebraic equations than the unknown coefficients. The multiple linear regression analysis, which is based on the least squares error norm, is employed to obtain the coefficients. Considering simple supported boundary conditions, an analogy between isotropic skew plates and rectangular anisotropic plates is used for solutions. The effect of fiber orientation is observed in clamped square and rectangular anisotropic plates. The results obtained by this method are compared with those obtained by other analytical methods.     

基于径向基函数神经网络优化制备复合改性钠基蒙脱土/橡胶复合材料及其补强-阻燃性能

以钠基蒙脱土作为研究对象,以十六烷基三甲基溴化铵与去离子水的混合液、硅烷偶联剂KH550与无水乙醇的混合液作为改性剂,制备复合改性钠基蒙脱土,并将复合改性钠基蒙脱土代替部分炭黑,与促进剂、硫磺、ZnO、硬脂酸、橡胶进行复合,制备复合改性钠基蒙脱土/橡胶复合材料。利用径向基函数（RBF）神经网络模型优化复合改性钠基蒙脱土/橡胶复合材料的制备工艺参数,并且对复合改性钠基蒙脱土和复合改性钠基蒙脱土/橡胶复合材料进行表征与测试。结果表明,当扩展系数为0.50~0.65时,所建立复合改性钠基蒙脱土/橡胶复合材料力学性能和阻燃性能的RBF神经网络模型具有最佳的逼近效果。当去离子水用量为1 074 g、十六烷基三甲基溴化铵用量为13.7 g、无水乙醇用量为14.8 g、硅烷偶联剂KH550用量为0.32 g、搅拌速度为2 890 r/min时,复合改性钠基蒙脱土/橡胶复合材料具有良好的力学性能和补强性能,即拉伸强度19.1 MPa、撕裂强度43.5 kN/m和极限氧指数32.83%。      

Self-adaptive radial basis function neural network for short-term electricity price forecasting

Effective and reliable electricity price forecast is essential for market participants in setting up appropriate risk management plans in an electricity market. A reliable price prediction model based on an advanced self-adaptive radial basis function (RBF) neural network is presented. The proposed RBF neural network model is trained by fuzzy c-means and differential evolution is used to auto-configure the structure of networks and obtain the model parameters. With these techniques, the number of neurons, cluster centres and radii of the hidden layer, and the output weights can be automatically calculated efficiently. Meanwhile, the moving window wavelet de-noising technique is introduced to improve the network performance as well. This learning approach is proven to be effective by applying the RBF neural network in predicting of Mackey-Glass chaos time series and forecasting of the electricity regional reference price from the Queensland electricity market of the Australian National Electricity Market.     

Efficient image compression techniques for compressing multimodal medical images using neural network radial basis function approach

Image compression technique is used to reduce the number of bits required in representing image, which helps to reduce the storage space and transmission cost. Image compression techniques are widely used in many applications especially, medical field. Large amount of medical image sequences are available in various hospitals and medical organizations. Large images can be compressed into smaller size images, so that the memory occupation of the image is considerably reduced. Image compression techniques are used to reduce the number of pixels in the input image, which is also used to reduce the broadcast and transmission cost in efficient form. This is capable by compressing different types of medical images giving better compression ratio (CR), low mean square error (MSE), bits per pixel (BPP), high peak signal to noise ratio (PSNR), input image memory size and size of the compressed image, minimum memory requirement and computational time. The pixels and the other contents of the images are less variant during the compression process. This work outlines the different compression methods such as Huffman, fractal, neural network back propagation (NNBP) and neural network radial basis function (NNRBF) applied to medical images such as MR and CT images. Experimental results show that the NNRBF technique achieves a higher CR, BPP and PSNR, with less MSE on CT and MR images when compared with Huffman, fractal and NNBP techniques.     

STEP-compliant NC research: the search for intelligent CAD/CAPP/CAM/CNC integration

Since the first generation of Numerically Controlled (NC) machine tools was developed in the 1950s, there have been many developments which make today's NC machines completely unrecognizable from their early ancestors. Further developments, however, are now being significantly limited by the current programming language (ISO6983 or RS274D) that has been supporting NC manufacture since day one. Today a new standard, informally known as STEP-NC, is being used as the basis for development of the next generation of Computer Numerically Controlled (CNC) controller. This new standard is ISO 14649 and ISO 10303 AP 238. This standard gives CAM and CNC vendors the opportunity to integrate the capabilities of CAD/CAM systems with a new breed of intelligent CNC controllers, which have bi-directional communication of information representing standardized geometric and manufacturing data. This paper presents a comprehensive review of STEP-NC developments for a range of CNC processes. These developments include some major projects collaborated at the international level as well as those carried out by different groups of researchers in countries such as Germany, Switzerland, UK, Korea, USA and New Zealand. This paper also tries to portray a futuristic view of STEP-NC applications for CAD, CAPP, CAM and CNC integration, identifies the issues and challenges for STEP-NC, and provides a vision of a STEP-NC-compliant process chain supported by a product and manufacturing model.     

Feature-driven,process-based approach to the integration of CAD /CAM in wireframe models

This paper presents a structure for an integrated CAD/CAM system in wireframe models than can be simultaneously used for product design and manufacture of prismatic components. A feature-driven, process-based design methodology, which derives its basis from traditional geometry-based design, feature-based design, deterministic and expert heuristic manufacturing knowledge, is proposed. This methodology provides an integrated modelling environment for automation of downstream manufacturing applications such as process planning and NC code generation. Representation of features in the design database is not limited to geometry alone but includes material, tooling and machine specifications and is based on a process-to-geometry mapping. Research shows that informational completeness of a CAD database depends on application; and for manufacturing tasks, the vocabulary of the process describing the means of making a part should be mapped directly to the feature-based geometry. This effort answers key issues in integrating CAD and CAM in the most widely used wireframe modelling systems.     

基于RBF神经网络的添加剂改性炭材料高温黏结剂的性能预报模型

在炭材料黏结剂添加剂改性实验数据的基础上，将神经网络方法用于研究添加剂配方和热处理温度对黏结强度的影响关系，建立了添加剂改性炭材料黏结剂的RBF（Radial Basis Function径向基函数）神经网络性能预报模型，并与BP（Back-Propagation逆传播）人工神经网络进行了预报精度和训练过程比较。结果表明：上述两种模型对于黏结强度的预报平均相对误差分别为0．0127和0．0600，且BP人工神经网络易陷入局部最小。因此，RBF神经网络模型的预报能力较好，得出了具有较精确黏结性能的添加剂配方和热处理数据。可望在炭材料黏结剂改性中的多变量、非线性体系中提高实验工作效率，为炭材料黏结剂提供一条有应用前景的理论设计途径。     

Classification of audio radar signals using radial basis function neural networks

Radial basis function (RBF) neural networks are used to classify real-life audio radar signals that are collected by a ground surveillance radar mounted on a tank. Currently, a human operator is required to operate the radar system to discern among signals bouncing off tanks, vehicles, planes, and so on. The objective of this project is to investigate the possibility of using a neural network to perform this target recognition task, with the aim of reducing the number of personnel required in a tank. Different signal classification methods in the neural net literature are considered. The first method employs a linear autoregressive (AR) model to extract linear features of the audio data, and then perform classification on these features, i.e, the AR coefficients. AR coefficient estimations based on least squares and higher order statistics are considered in this study. The second approach uses nonlinear predictors to model the audio data and then classifies the signals according to the prediction errors. The real-life audio radar data set used here was collected by an AN/PPS-15 ground surveillance radar and consists of 13 different target classes, which include men marching, a man walking, airplanes, a man crawling, and boats, etc. It is found that each classification method has some classes which are difficult to classify. Overall, the AR feature extraction approach is most effective and has a correct classification rate of 88% for the training data and 67% for data not used for training.     

Adaptive, optical, radial basis function neural network for handwritten digit recognition

An adaptive, optical, radial basis function classifier for handwritten digit recognition is experimentally demonstrated. We describe a spatially multiplexed system that incorporates an on-line adaptation of weights and basis function widths to provide robustness to optical system imperfections and system noise. The optical system computes the Euclidean distances between a 100-dimensional input vector and 198 stored reference patterns in parallel by using dual vector-matrix multipliers and a contrastreversing spatial light modulator. Software is used to emulate an electronic chip that performs the on-line learning of the weights and basis function widths. An experimental recognition rate of 92.7% correct out of 300 testing samples is achieved with the adaptive training, versus 31.0% correct for nonadaptive training. We compare the experimental results with a detailed computer model of the system in order to analyze the influence of various noise sources on the system performance.     

A method for simulation based optimization using radial basis functions

We propose an algorithm for the global optimization of expensive and noisy black box functions using a surrogate model based on radial basis functions (RBFs). A method for RBF-based approximation is introduced in order to handle noise. New points are selected to minimize the total model uncertainty weighted against the surrogate function value. The algorithm is extended to multiple objective functions by instead weighting against the distance to the surrogate Pareto front; it therefore constitutes the first algorithm for expensive, noisy and multiobjective problems in the literature. Numerical results on analytical test functions show promise in comparison to other (commercial) algorithms, as well as results from a simulation based optimization problem.     

Smart chemical taste sensor for determination and prediction of taste qualities based on a two-phase optimized radial basis function network

A novel, smart, chemical taste sensor that realistically mimics the behavior of the human gustatory system is described. The taste sensor consists of an array of electrochemical sensors that represent the gustatory receptors on the human tongue, and a two-phase optimized radial basis function network (RBFN) to represent the human brain, which comprehensively analyzes the gustatory stimulation and judges the overall taste. In the illustrated model, eight electrodes were fabricated to determine the eight major taste-causing substances, Na+, K+, Cl-, H+, sucrose, glucose, glutamate, and caffeine. The detected signals were fed to a two-phase RBFN optimized by the implementation of a basis optimization algorithm and weight decay term for appropriate data processing. The first phase of the two-phase RBFN quantifies the amount of taste-causing substances in food samples from the responses of the electrodes. These results are then fed to the second phase, which correlates the amount of substances with the overall taste. The final output is scored on a scale of 1-5 for each of the five basic tastes sensed by the human gustatory system, which are saltiness, sourness, sweetness, bitterness, and umami. The constructed network estimated the intensity of the basic tastes of 30 drink varieties with an average relative error of 7.0% compared to the human scores. The network could also estimate the variance in the human sensory perception. Moreover, the sensor successfully predicted the interactions of tastes such as suppression of bitterness by sweetness and enhancement of umami by saltiness, which are illusions sensed by the human gustatory system. With these abilities, the novel taste sensor can be considered as a quantitative yet humanlike sensor with a great potential for practical applications.