钛及钛合金板材冲压成形及有限元模拟

综述了钛及钛合金薄板的塑性及冲压成形性,在此基础上研究了钛板冲压成形的影响因素,着重分析了压边力、模具尺寸对钛板冲压成形的影响。然后介绍了有限元模拟的基本原理及壳单元、本构方程的选择,并利用DYNAFORM软件模拟了TA2纯钛半球形工件的成形过程,并对压边力进行优化,得出最适压边力范围为27~37 kN。     

计算机仿真中压边力对薄板成形的影响

在薄板冲压成形过程中,压边力大小对成形件的质量有着直接的影响。采用功能强大的Dy-naform软件来模拟薄板的成形过程,并用图表的形式来分析压边力对薄板成形的影响,从而为实际生产提供一种确定压边力的方法。     

基于Autoform软件的汽车行李箱板冲压成形模拟

基于Autoform软件,讨论了汽车覆盖件参数化设计的方法,包括冲压方向、压料面、工艺补充面、等效拉延筋的生成方法。通过汽车行李箱板冲压成形过程仿真,得出可能出现的破裂、起皱等板料缺陷,调整压边力、等效拉延筋等工艺参数,使产品冲压成形工艺达到优化。结果表明,利用Autoform软件能快速地确定压边力大小,优化板料尺寸和形状,有利于减小产品研发周期、提高产品质量。     

圆锥形零件冲压成形皱曲和破裂三极限的预报与控制

根据变形区“多余三角形材料皱曲模型”和防皱曲压边力分别导出外皱曲极限预报与控制判据和内皱曲极限预报与控制判据,以及防外皱曲压边装置的选择判据和防内皱曲压边装置的选择判据。依据破裂模型和拉深变形力导出破裂极限的预报与控制判据,最后给出了圆锥形零件冲压成形皱曲和破裂三极限的预报与控制判据和图形。依据选定的压边力可计算出拉深变形力。实验验证和对比结果证明,将上述判据用于圆锥形零件冲压成形皱曲和破裂三极限的预报与控制是相当准确的     

基于Yoshida材料模型的帽形件回弹仿真分析

以1. 2 mm厚HC340/590DP帽形件为研究对象,采用Dynaform冲压仿真和冲压试验相结合的方法,探究了压边力、成形间隙、翻边角度等工艺参数对回弹的影响。结果显示,采用考虑随动硬化的Yoshida材料模型计算的回弹结果与试验测量结果的变化趋势一致,成形、翻边模拟结果精度在75%以上。对于帽形件的回弹,改变成形工艺对回弹量的影响最大,调整成形间隙或翻边角度也有一定影响,但在一定范围内调整压边力对回弹量的影响不大。     

压边力和液压力对椭球形件充液拉深成形的影响

起皱和破裂是板材零件的两大缺陷。充液拉深成形是一种新的塑性加工工艺,可以改善曲面形零件的成形质量。压边力和液压力是该工艺的关键参数。利用Dynaform有限元分析软件研究了旋转椭球形件在恒压边力变液压力、变压边力变液压力情况下的充液拉深成形情况。模拟结果表明,压边力和液压力的加载路径对于零件的成形有明显的影响,采用变压边力变液压力技术可以得到不破裂、不起皱、减薄率小、厚度均匀的零件,在很大程度上改善了零件的成形效果。     

热冲压工艺参数对零件成形性影响规律分析

主要研究了热冲压工艺参数对热冲压后零件成形性的影响规律。热冲压工艺参数主要包括冲压速度、压边力、摩擦因数和板料温度,每个工艺参数均分为低、中、高3个水平。结合测试方案设计和仿真模型,利用方差分析研究不同工艺参数对零件成形性的影响程度;同时,利用量化分析方法确定不同水平的工艺参数对零件成形性影响程度的量化值。结果表明,工艺参数对零件减薄率的影响程度由重到轻的顺序为摩擦因数、板料温度、冲压速度和压边力,其中摩擦因数影响程度的量化值约为97.3%。     

汽车板用高强钢冲压回弹的数值模拟与研究

汽车板用高强钢回弹现象影响零件的形状与尺寸精度,回弹问题与材料的性能、冲压工艺有很大关系。利用Dyna Form数值软件,以邯钢汽车板的实际性能参数作为输入条件,分析了不同强度级别(HC340/590DP、HC210IF、DC06)、不同厚度(1.0 mm、1.5 mm、2.0 mm)、不同压边力(6~30 t)等参数对U形件回弹性能的影响规律。试验结果:材料的回弹量随着材料强度和厚度的增加而增加;在一定的压边力范围内,回弹量与压边力呈线性关系,压边力越大,材料变形越充分,回弹量也就越小。     

半球形件变液压力变压边力充液拉深研究

采用充液拉深工艺,运用变液压力变压边力组合的方法,以DYNAFORM软件为平台,对半球形件的成形过程进行了有限元仿真模拟.仿真结果表明,在恒定压边力充液拉深下,零件易发生起皱和破裂,零件减薄率较大,无法满足成形要求.采用变液压力和变压边力组合的加载方式进行研究.研究结果发现,采用变液压力变压边力组合进行充液拉深的零件不破裂、不起皱、减薄率小,零件厚度分布均匀,能够较大程度地改善零件的成形效果.最后通过试验验证了该工艺的可行性.     

一种铝合金前罩内板结构的冲压可行性研究

针对自主设计的某款铝合金前罩内板结构形式进行冲压成形可行性分析,通过调整冲压方向、压边力、拉延系数及等效拉延筋等影响因子,根据仿真结果分析验证该结构形式的可成形性,为铝合金前罩内板工程化提供设计参考。     

基于GRNN网络的中厚板轧制温度的预测

 针对中厚板轧机控制模型中的轧制温度精度的提高问题,以4200轧机轧制的大量实测数据为基础,利用Matlab人工神经网络工具箱,建立了中厚板轧制温度的GRNN神经网络预测模型。通过分析影响钢板温度变化的各种因素,调整神经网络的光滑因子,确定了最佳的网络结构形式,提高了模型的预测精度,并与传统的BP神经网络模型相比较。结果表明,GRNN网络具有更高的精度和更好的泛化能力。该神经网络模型可应用于中厚板轧制温度的预测,也可为人工神经网络在其它自动控制方面的应用提供参考。     

Prediction of roll force in skin pass rolling using numerical and artificial neural network methods

A combination of finite element method and neural network methods was used for rapid prediction of the roll force during skin pass rolling of 980DP and 1180CP high strength steels. The FE based commercial package DEFOEM-2D was used to develop a mathematical model of the skin pass rolling operation. Numerical experiments were designed with different process parameters to produce training data for a neural network algorithm. The friction coefficient was considered as an input parameter in the neural network but it was optimised using an iterative method employing an equation that relates the friction coefficient to the rolling force. The load prediction method described in this paper is sufficiently rapid that it can be used in real-time as an adjustment tool for skin pass rolling mills with error within 10% (based on plant data from POSCO).     

Application of autonomous neural network systems to medical pattern classification tasks

This paper presents a study of the application of autonomously learning multiple neural network systems to medical pattern classification tasks. In our earlier work, a hybrid neural network architecture has been developed for on-line learning and probability estimation tasks. The network has been shown to be capable of asymptotically achieving the Bayes optimal classification rates, on-line, in a number of benchmark classification experiments. In the context of pattern classification, however, the concept of multiple classifier systems has been proposed to improve the performance of a single classifier. Thus, three decision combination algorithms have been implemented to produce a multiple neural network classifier system. Here the applicability of the system is assessed using patient records in two medical domains. The first task is the prognosis of patients admitted to coronary care units; whereas the second is the prediction of survival in trauma patients. The results are compared with those from logistic regression models, and implications of the system as a useful clinical diagnostic tool are discussed.     

Relationship of actin, microtubules, and crosswall synthesis during septation in Aspergillus nidulans

A significant limitation of neural networks is that the representations they learn are usually incomprehensible to humans. We have developed an algorithm, called TREPAN, for extracting comprehensible, symbolic representations from trained neural networks. Given a trained network, TREPAN produces a decision tree that approximates the concept represented by the network. In this article, we discuss the application of TREPAN to a neural network trained on a noisy time series task: predicting the Dollar-Mark exchange rate. We present experiments that show that TREPAN is able to extract a decision tree from this network that equals the network in terms of predictive accuracy, yet provides a comprehensible concept representation. Moreover, our experiments indicate that decision trees induced directly from the training data using conventional algorithms do not match the accuracy nor the comprehensibility of the tree extracted by TREPAN.     

Assessment of quantitative artificial neural network analysis in a metabolically dynamic ex vivo 31P NMR pig liver study

Quantitative artificial neural network analysis for 1550 ex vivo 31P nuclear magnetic resonance spectra from hypothermically reperfused pig livers was assessed. These spectra show wide ranges of metabolite concentrations and have been analyzed using metabolite prior knowledge based lineshape fitting analysis which had proved robust in its biochemical interpretation. This finding provided a good opportunity to assess the performance of artificial neural network analysis in a biochemically complex situation. The results showed high correlations (0.865 < or = R < or = 0.992) between the lineshape fitting and artificial neural network analysis for the metabolite values, and the artificial neural network analysis was able to fully represent the trends in the metabolic fluctuations during the experiments.     

用人工神经网络模型预测高碳钢高速线材力学性能

以现场正交试验数据为基础，采用人工神经网络方法预测高碳钢高碳钢高速线材产品力学性能，将预报结果与试验结果相比较可知，该模型具有较高的精度。     

基于灰色理论的铁水预处理终点磷含量神经网络预测模型

 在传统BP神经网络预测模型的基础上,依据灰色理论中的灰色关联度,提出了输出变量各个影响因素的灰色关联度权值,首次建立基于灰色理论的神经网络预测模型,并依据国内某钢厂300组实际生产数据进行仿真试验。试验结果表明：误差绝对值小于5%的炉数有39炉,占总炉数的65.00%;误差绝对值小于10%的炉数共有58炉,占到96.67%。与传统BP神经网络相比,基于灰色理论的神经网络模型的预测精度提高近12.5%,说明基于灰色理论的铁水预处理终点磷含量神经网络预测模型能更精确地反映现场实际水平。     

Motor cortical reflex myoclonus: a case study with MEG

The need for a reliable method of predicting movement during anesthesia has existed since the introduction of anesthesia. This paper proposes a recognition system, based on the autoregressive (AR) modeling and neural network analysis of the electroencephalograph (EEG) signals, to predict movement following surgical stimulation. The input to the neural network will be the AR parameters, the hemodynamic parameters blood pressure (BP) and heart rate (HR), and the anesthetic concentration in terms of the minimum alveolar concentration (MAC). The output will be the prediction of movement. Design of the system and results from the preliminary tests on dogs are presented in this paper. The experiments were carried out on 13 dogs at different levels of halothane. Movement prediction was tested by monitoring the response to tail clamping, which is considered to be a supramaximal stimulus in dogs. The EEG data obtained prior to tail clamping was processed using a tenth-order AR model and the parameters obtained were used as input to a three-layer perceptron feedforward neural network. Using only AR parameters the network was able to correctly classify subsequent movement in 85% of the cases as compared to 65% when only hemodynamic parameters were used as the input to the network. When both the measures were combined, the recognition rate rose to greater than 92%. When the anesthetic concentration was added as an input the network could be considerably simplified without sacrificing classification accuracy. This recognition system shows the feasibility of using the EEG signals for movement during anesthesia.     

Neural Networks and Principal Component Analysis for Identification of Building Natural Periods

This paper deals with an application of neural networks for computation of fundamental natural periods of buildings with load-bearing walls. The analysis is based on long-term tests performed on actual structures. The identification problem is formulated as the relation between structural and soil basement parameters, and the fundamental period of building. The principal component analysis for compression of input data is also used. Backpropagation neural networks are applied in the analysis. Results of neural network identification of natural periods are compared with data from experiments. The application of the proposed neural networks enables us to identify the natural periods of the buildings with quite satisfactory accuracy for engineering practice. The compression of the input data to principal components by principal component analysis makes it possible to design much smaller neural networks than those without data compression with no greater increase of the neural approximation errors. It appears that this technique would also be very useful in damage detection and health monitoring of structures.     

基于BP神经网络的冷轧带钢力学性能预测

利用BP神经网络方法预测SPCC冷轧带钢产品力学性能并以现场正交试验数据为基础,对比预报结果和试验结果可以知道,该模型具有较高的精度,适用于现场生产。