PowerMILL在汽车喇叭安装板拉延模具中的应用

本文分析了汽车喇叭安装板拉延模具的特点,介绍了应用PowerMILL软件对汽车覆盖件拉延模具数控加工工艺的规划,并说明了数控编程中加工策略的选择及参数的设置.     

模具数控加工CAM编程中工艺参数的确定

众所周知,在借助CAM软件进行数控编程的过程中,工艺参数的选择十分重要,它不仅对被加工零件的质量影响巨大,甚至可以决定着机床功效的发挥和安全生产的顺利进行。本文作者针对模具零件的特点,分析了模具零件数控铣削加工编程中工艺参数的选择对加工质量的影响,并结合实际介绍了模具数控加工中CAM编程时工艺参数的设定方法和原则。     

车身覆盖件模具数控加工工艺方案的确定

汽车覆盖件模具是汽车生产中非常重要的工艺装备，其型面多为立体曲面，并且结构复杂．品种繁多．因此在现代模具生产中多采用数控加工的方法。在采用数控加工时，确定怎样的加工方法，也就是如何进行数控加工，对整套模具均有较大影响，甚至由于无法确定合适的加工方法．而不能利用现有机床进行数控加工，或由于方法选择不当，造成往返工作与加工误差的增多。因此，合理地确定数控加工方法，是制订数控加工工艺的基础。本结合一汽模具中心近年来制造各类汽车覆盖件模具中的一些典型结构，论述了在模具数控加工中如何合理地确定数控加工方法：     

基于 PowerMILL的汽车覆盖件模具的数控编程

作者在文中对汽车覆盖件模具的特点进行了详细分析，并以哈飞松花江HF10车尾门外板模具制造为例介绍了PowerMILL软件的具体应用过程。文章给出了该模具数控加工工艺的规划及其数控编程中加工策略选择和参数的设置，对其他汽车覆盖件模具的加工有一定的指导意义。     

PowerMILL在标准化锻模锁扣中的应用

本文从锻造模具锁扣加工中存在的问题入手,分析了锻造模具锁扣存在的设计和加工问题,并且分析了所有锁扣的类型和具体的尺寸,从而总结归纳了锁扣的设计标准和数控精加工锁扣的加工参数标准,提高了锁扣的加工效率,减少了模具的加工时间。     

车身覆盖件模具CAD/CAM曲面造型方法

曲面造型是实现车身覆盖件模具数控加工的基础。曲面造型合理与否将直接影响数控加工的质量。本文介绍了与曲面造型有关的参数曲线、参数曲面、等参数线等概念,并讨论了曲线参数化方法及各种曲面的造型方法,重点论述保证曲面参数对应性和曲面光顺性的方法。     

辊锻模具的数控加工

本文分析了辊锻模具结构特点,并介绍了辊锻模具数控加工技术、探讨加工策略和数控加工刀具匹配等,满足辊锻模具型槽加工效率和精度要求。     

三维型面造型和数控加工

本文通过对小音箱前面板模具的凸模进行三维造型和数控加工的研究,给出了型面三维造型和数控加工技术相结合在模具制造中的应用步骤.造型设计与数控加工有机的结合,对小型模具加工将是很好的选择.     

三维型面造型和数控加工

本文通过对小音箱前面板模具的凸模进行三维造型和数控加工的研究,给出了型面三维造型和数控加工技术相结合在模具制造中的应用步骤。造型设计与数控加工有机的结合,对小型模具加工将是很好的选择。     

模具数控加工质量的因素分析

模具数控加工所占比例不断增加及产品质量要求不断提高,都对产品的制造精度提出了更高的要求,本文通过对模具的总结,分析了影响数控加工质量的相关因素;从机床、刀具、加工工艺、软件和人等方面介绍了提高模具数控加工质量的思路。     

Global parameter optimization for biokinetic modeling of simple batch experiments

Environmental process modeling is challenged by the lack of high quality data, stochastic variations, and nonlinear behavior. Conventionally, parameter optimization is based on stochastic sampling techniques to deal with the nonlinear behavior of the proposed models. Despite widespread use, such tools cannot guarantee globally optimal parameter estimates. It can be especially difficult in practice to differentiate between lack of algorithm convergence, convergence to a non-global local optimum, and model structure deficits. For this reason, we use a deterministic global optimization algorithm for kinetic model identification and demonstrate it with a model describing a typical batch experiment. A combination of interval arithmetic, reformulations, and relaxations allows globally optimal identification of all (six) model parameters. In addition, the results suggest that further improvements may be obtained by modification of the optimization problem or by proof of the hypothesized pseudo-convex nature of the problem suggested by our results.     

Modeling asphalt pavement overlay transverse cracks using the genetic operation tree and Levenberg-Marquardt Method

The Artificial Neural Network (ANN) and the nonlinear regression method are commonly used to build models from experimental data. However, the ANN has been criticized for incapable of providing clear relationships and physical meanings, and is usually regarded as a black box. The nonlinear regression method needs predefined and correct formula structures to process parameter search in terms of the minimal sum of square errors. Unfortunately, the formula structures of these models are often unclear and cannot be defined in advance. To overcome these challenges, this study proposes a novel approach, called “LMGOT,” that integrates two optimization techniques: the Levenberg-Marquardt (LM) Method and the genetic operation tree (GOT). The GOT borrows the concept from the genetic algorithm, a famous algorithm for solving discrete optimization problems, to generate operation trees (OTs), which represent the structures of the formulas. Meanwhile, the LM takes advantage of its merit for solving nonlinear continuous optimization problems, and determines the coefficients in the GOTs that best fit the experimental data. This paper uses the LMGOT to investigate the data sets of pavement cracks from a 15-year experiment conducted by the Texas Departments of Transportation. Results show a concise formula for predicting the length of pavement transverse cracking, and indicate that the LMGOT is an efficient approach to building an accurate crack model.     

Generation of optimal artificial neural networks using a pattern search algorithm: application to approximation of chemical systems

A pattern search optimization method is applied to the generation of optimal artificial neural networks (ANNs). Optimization is performed using a mixed variable extension to the generalized pattern search method. This method offers the advantage that categorical variables, such as neural transfer functions and nodal connectivities, can be used as parameters in optimization. When used together with a surrogate, the resulting algorithm is highly efficient for expensive objective functions. Results demonstrate the effectiveness of this method in optimizing an ANN for the number of neurons, the type of transfer function, and the connectivity among neurons. The optimization method is applied to a chemistry approximation of practical relevance. In this application, temperature and a chemical source term are approximated as functions of two independent parameters using optimal ANNs. Comparison of the performance of optimal ANNs with conventional tabulation methods demonstrates equivalent accuracy by considerable savings in memory storage. The architecture of the optimal ANN for the approximation of the chemical source term consists of a fully connected feedforward network having four nonlinear hidden layers and 117 synaptic weights. An equivalent representation of the chemical source term using tabulation techniques would require a 500 x 500 grid point discretization of the parameter space.     

基于神经网络和专家系统的铸造质量控制方法

提出了一种基于神经网络与专家系统,具有一定自学习能力的铸造质量控制方法,具体描述了其工作原理.该系统由工艺参数优化模块和缺陷诊断模块组成.工艺参数优化模块以神经网络为推理机,以过去生产数据和有限元数值模拟数据作为训练样本,建立工艺参数与铸件性能之间的非线性关系.缺陷诊断模块以基于产生式规则的推理方式,诊断缺陷类型、产生原因及防治措施,且诊断结果反馈到工艺参数优化模块,用于神经网络再学习.试验结果表明,该系统提高了工艺参数准确率,增强了缺陷诊断能力,减少了铸件次品率.     

基于可视化计算的图形建模及参数寻优环境的设计与实现

研究了复杂非线性系统参数优化环境的可视化建模技术及软件实现问题．应用先进的仿真技术,采用面向对象和结构化设计方法,通过通用软件实现了复杂系统参数自动寻优环境的高度可视化的一体化;与基于常规高级和谐设计语言的参数寻优方法相比,该方法不仅不用传统程序代码对算法编程,而且可方便地对系统进行多参数自动迭代寻优试验及进行智能化分析．     

人工神经网络在木材损伤识别中的应用

利用人工神经网络(ANN)对含有不同缺陷程度的木材进行了定性和定量解析.通过选择合理的神经网络结构,建立有效的训练样本集,确定合理的参数及训练方法,对3种不同缺陷类型木材进行了解析,考察了网络的泛化能力.结果表明:该网络能够对不同缺陷程度的木材进行准确地识别,而且本研究为人工神经网络在木材缺陷损伤的定性和定量分析方面提供了一种有效的方法.     

基于遗传算法的半导体制冷器非线性PID设计

针对常规线性PID对具有非线性特征的半导体制冷器温度控制系统存在快速性和超调量难以兼得、抗干扰能力差的问题,提出将非线性PID控制用于半导体制冷器温度控制的策略.通过对线性PID存在问题以及PID各增益参数与偏差信号之间非线性关系的分析,构建了增益参数的非线性函数,针对非线性函数中参数较多问题提出了自适应遗传寻优的求解方法.仿真和实验结果表明,基于此遗传算法寻优的非线性PID控制器相比线性PID控制器,调节时间缩短,超调量减小,抗干扰能力更强.     

Artificial neural networks to optimize the extrusion of an aluminium alloy

Extrusion of aluminium alloys is a complex process which depends on the characteristics of the material and on the process parameters (initial billet temperature, extrusion ratio, friction at the interfaces, die geometry etc.). The temperature profile at the die exit, largely influences microstructure, mechanical properties, and surface quality of an extruded product, consequently it is the most important parameter for controlling the process. In turn the temperature profile depends on other process variables whose right choice is fundamental to avoid surface damage of the extruded product. In the present work, two neural networks were implemented to optimize the aluminium extrusion process determining the temperature profile of an Al 6060 alloy (UNI 9006/1) at the exit of induction heater (ANN1) and at the exit of the die (ANN2). The three-layer neural networks with Levemberg Marquardt algorithm were trained with the experimental data from the industrial process. The temperature profiles, predicted by the neural network, closely agree with experimental values.     

A parameter optimization method for radial basis function type models

This paper considers the nonlinear systems modeling problem for control. A structured nonlinear parameter optimization method (SNPOM) adapted to radial basis function (RBF) networks and an RBF network-style coefficients autoregressive model with exogenous variable model parameter estimation is presented. This is an off-line nonlinear model parameter optimization method, depending partly on the Levenberg-Marquardt method for nonlinear parameter optimization and partly on the least-squares method using singular value decomposition for linear parameter estimation. When compared with some other algorithms, the SNPOM accelerates the computational convergence of the parameter optimization search process of RBF-type models. The usefulness of this approach is illustrated by means of several examples.     

Implementation of regularization for separable nonlinear least squares problems

This paper considers a class of separable nonlinear least squares problems in which a model can be represented as a linear combination of nonlinear functions. A regularized nonlinear parameter optimization approach is presented for coping with the potential ill-conditioned problem of parameter divergence. Together with a regularization parameter detection technique, Tikhonov regularization and truncated singular value decomposition are utilized in the estimation of the linear parameters if the nonlinear parameters are changed during the parameter optimization process, which centers on a nonlinear parameter search using the Levenberg-Marquardt algorithm. Benefiting from the regularization in parameter optimization, the potential ill-conditioned issue can be avoided, and the multi-step-ahead forecasting accuracy of the estimated model may be largely improved. The usefulness of this approach is illustrated by means of a chaotic time-series prediction and nonlinear industrial process modeling.