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1.
机械加工中的误差复映现象使加工参数的选择主要依靠操作人员的经验。在分析误差复映问题模型的基础上,提出了一种可同时输入精确值和模糊语言真值的混合输入型模糊系统。利用混合输入型模糊系统的非线性逼近能力,结合专家经验实现机械加工参数的优化选择。对建立的模糊系统可以通过选择精确值或模糊语言真值输入加工前毛坯误差、工艺系统刚度等加工条件,系统能够输出满足加工要求的加工次数和各次加工量等加工参数。实验验证了用混合输入型模糊逻辑系统实现加工参数优化的可行性。  相似文献   

2.
提出一种用人工神经网络解决机械加工中的误差复映问题的方法。分析了误差复映产生的机理,利用人工神经网络的非线性映射能力对输入样本进行分类,并用模糊理论对输出结果进行处理。对模糊理论和神经网络在机械加工中的应用作了一定的探索。  相似文献   

3.
在分析目前常用反向传播算法改进方法优缺点的基础上,提出用共轭梯度法对自适应模糊神经推理系统进行改进的训练算法,在训练中用Fletcher-Reeves方法计算上次搜索方向对新搜索方向的影响因数,在混沌时间序列预测和复杂非线性函数逼近的应用实例证明,改进后的算法收敛次数减少,训练速度加快.结合MATLAB的模糊工具箱,详述了如何在已有标准算法基础上进行算法改进.目前计算机辅助工艺设计受诸多复杂非线性问题的困扰发展缓慢,利用自适应模糊神经推理系统的自学习、自适应和逻辑推理能力,将改进后的算法用于逼近误差复映系数与工艺系统刚度、进给量等因素之间的非线性关系,实现机械加工参数的优化,提高工艺系统的自适应能力和工作效率,试验验证了此方法的可行性.  相似文献   

4.
为实现计算机辅助工艺计划(CAPP)系统中零件特征加工方法的决策,分析了影响零件加工方法的因素,提出利用模糊BP神经网络算法对零件特征加工方法链进行优化决策.根据BP网络能够学习任何模式的映射关系的特点,建立了从输入到输出的网络决策模型,并对网络结构、参数确定和样本选取等问题进行详细阐述,运用合理的学习算法来训练网络,最后通过实例验证了该网络的有效性.结果表明,利用模糊BP神经网络进行零件加工方法的选择是有效的和可靠的.  相似文献   

5.
在综合分析工件材料、材料状态、表面硬度和要求达到的加工表面质量与油石各参数之间关系的基础上,采用改进的GCAQBP人工神经网络算法,通过对输入输出参数进行编码优化,构建了不锈钢材料珩磨加工油石特性参数智能选择模型.通过实验研究,证明了该智能选择模型与传统经验选择相比,具有选择速度快、可靠性高等优点.课题研究为珩磨加工油石特性参数的选择提供了一种新的智能方法.  相似文献   

6.
分析了误差的来源和传递方式,针对机械加工过程高度非线性、多输入和多输出的特点,构造了双隐层L-M算法BP神经网络误差预测控制模型.根据工艺系统刚度、工件硬度、加工前、后径向误差来预测控制刀具径向总进刀量、第一、第二次刀具径向进刀量,实验和仿真结果表明该模型能指导生产、优化加工工艺和提高产品质量.最后,采用LAB-VIEW软件和MATLAB软件编制了误差预测控制系统,实现了预测控制的可视化.  相似文献   

7.
针对陶瓷等难加工材料的精密加工要求与特点以及球面磨削传统加工模式,分析了氮化硅陶瓷材料球面廓形工件砂轮法向跟踪精密磨削的方法。采用正交试验法设计试验,运用极差法和方差法综合分析相关磨削工艺参数对工件加工质量与效率的影响规律。考虑到当前磨削加工工艺方案选择与优选的难点,利用遗传神经网络算法建立了工件加工质量与效率和相关磨削工艺参数之间的非线性映射关系,并基于正交试验法的分析结果对遗传神经网络算法进行了改进,实现了相关磨削工艺参数的优化,缩短了氮化硅陶瓷材料球面廓形工件数控磨削工艺制定与操作的时间,提高了磨削加工质量和效率。  相似文献   

8.
为了消除或减小磁滞非线性特性对磁控形状记忆合金驱动器定位精度的影响,应用BP神经网络建立了磁控形状记忆合金驱动器磁滞模型。针对BP网络算法存在的不足,以及网络结构、初始连接权值和阈值的选择对BP网络训练的影响很大等问题,提出一种混合遗传算法对神经网络磁滞模型的权值和阈值进行优化。将优化后的参数赋值给BP神经网络重新训练,结果表明,优化后的磁滞模型训练误差绝对值由25nm减小到5nm,有较好的收敛性。  相似文献   

9.
铝合金板快速加热弯曲的参数预测   总被引:3,自引:3,他引:0  
基于BP神经网络平台,建立了铝合金板快速加热弯曲的角度预测BP网络模型,实现了脉冲激光加工工艺的参数控制与优化。通过试验获得样本数据,将试验样本数据用于BP网络的训练,利用训练好的BP网络对非线性的样本数据规律进行拟合,对脉冲激光弯曲角度和工艺参数进行准确的预测,预测误差范围可控制在<5~8%,研究结果为实际生产中精密成形提供了有效的理论与试验依据。  相似文献   

10.
车床上车削外圆时,存在误差复映现象,工件毛坯的圆度误差会形成工件加工后的圆度误差。误差复映的大小可用误差复映系数衡量。误差复映系数与工艺系统刚度成反比,与径向切削力系数成正比。在实际生产中,当选定了机床、夹具、刀具和加工对象时,工艺系统的刚度是一个较稳定的值,基于此,从减少径向切削力系数着手,减少误差复映。径向切削力系数与切削速度υ、进给量f、车刀主偏角kr等参数有关。通过正交试验研究,确定了能减少圆度误差复映的合理切削用量和刀具主偏角。  相似文献   

11.
基于进化神经网络的刀具寿命预测   总被引:1,自引:0,他引:1  
为预测道具寿命,引入人工神经网络技术,建立了刀具寿命预测神经网络模型,同时对切削参数进行优化选择.在刀具寿命预测中,针对反向传播算法存在收敛速度慢、容易陷入局部极小值及全局搜索能力弱等缺陷,采用遗传算法训练反向传播神经网络,设计了进化神经网络的学习算法.实验和仿真结果表明:基于进化计算的反向传播神经网络可以克服单纯使用反向传播网络易陷入局部极小值等难题,刀具寿命的预测精度较高,从而为刀具需求计划制定、刀具成本核算,以及切削参数制定提供理论依据,节约了制造执行系统中的生产成本.  相似文献   

12.
数控电火花线切割加工参数优选的试验研究   总被引:1,自引:0,他引:1  
针对数控高速走丝电火花线切割加工中的电参数的选取,本文运用二次通用旋转组合设计方法进行了工艺数据试验,提出了针对人工神经网络建模的数据预处理方法,建立了基于BP神经网络的电火花线切割加工参数模型。该模型可有效地反映高速走丝电火花线切割加工的工艺规律,实现在指定加工要求下的加工参数的优化选取。  相似文献   

13.
为了分析刀具正常磨损后铣削颤振稳定域和表面位置误差,对刀具不同磨损状态下的切削力系数进行辨识,基于全离散法研究刀具正常磨损后铣削颤振稳定域和表面位置误差特性。发现当刀具正常磨损后,铣削系统的稳态临界切深呈现上升的趋势;随着工件表面洛氏硬度的提高,铣削系统稳态临界切深逐步下降,刀具正常磨损后临界切深与后刀面无磨损临界切深的差别逐步变小;在稳定域的局部会出现表面位置误差增加的情况。试验表明,该理论模型可以有效优化刀具正常磨损后的加工参数。  相似文献   

14.
Wire electro-discharge machining (WEDM) is a vital process in manufacturing intricate shapes. The present work proposes a semi-empirical model for material removal rate in WEDM based on thermo-physical properties of the work piece and machining parameters such as pulse on-time and average gap voltage. The model is developed by using dimensional analysis and non-linear estimation technique such as quasi-Newton and simplex. Predictability of the proposed model is more than 99% for all work materials studied. The work materials were silicon carbide particulate reinforced aluminium matrix composites. The experiments and model prediction show significant role of coefficient of thermal expansion in WEDM of these materials. In addition, an empirical model, based on response surface method, has also been developed. The comparison of these models shows significant agreement in the predictions.  相似文献   

15.
Electric discharge machining (EDM) is a highly promising machining process of ceramics. This research is an out of the paradigm investigation of EDM on Si3N4-TiN with Copper electrode. Ceramics are used for extrusion dies and bearing balls and they are more efficient, effective and even have longer life than conventional metal alloys. Owing to high hardness of ceramic composites, they are almost impossible to be machined by conventional machining as it entirely depends on relative hardness of tool with work piece. Whereas EDM offers easy machinability combined with exceptional surface finish. Input parameters of paramount significance such as current (I), pulse on (Pon) and off time (Poff), Dielectric pressure (DP) and gap voltage (SV) are studied using L25 orthogonal array. With help of mean effective plots the relationship of output parameters like Material removal rate (MRR), Tool wear rate (TWR), Surface roughness (Ra), Radial overcut (ROC), Taper angle (α), Circularity (CIR), Cylindricity (CYL) and Perpendicularity (PER) with the considered input parameters and their individual influence were investigated. The significant machining parameters were obtained by Analysis of variance (ANOVA) based on Grey relational analysis (GRA) and value of regression coefficient was determined for each model. The results were further evaluated by using confirmatory experiment which illustrated that spark eroding process could effectively be improved.  相似文献   

16.
Parallel kinematic machine tools (PKM) have the advantages of higher stiffness, higher payload capacity and lower inertia. Still their penetration into the machine tool industry is very less. One of the difficulties in using PKMs such as hexapod machine tools is that the stiffness continuously varies with configuration change at every instant. This makes location of work piece and selection of machining parameters difficult and complicated. A methodology is presented in this article to select optimal machining parameters for hexapod machine tools. Particle swarm optimization is used as a tool in the optimization process. A profile-milling example is also presented to demonstrate the selection of machining parameters.  相似文献   

17.
Electrical Discharge Machining (EDM) is very popular for machining conductive metal matrix composites (MMCs) because the hardness rendered by the ceramic reinforcements to these composites causes very high tool wear and cutting forces in conventional machining processes. EDM requires selection of a number of parameters for desirable results. Inappropriate parameter selection can lead to high overcuts, tool wear, excessive roughness, and arcing during machining and adversely affect machining quality. Arcing leads to short circuit gap conditions resulting in large energy discharges and uncontrolled machining. Arcing is a detrimental phenomenon in EDM which causes spoiling of workpiece and tool electrode and tends to damage the power supply of EDM machine. Parameter combinations that lead to arcing during machining have to be identified and avoided for every tool, work material, and dielectric combination. Proper selection of parameter combinations to avoid arcing is essential in EDM. In the work, experiments were conducted using L27 design of experiment to determine the parameter settings which cause arcing in EDM machining of TiB2p reinforced ferrous matrix composite. Important EDM process parameters were selected in roughing, intermediate, and finishing range so as to study the occurrence of arcing. Using the experimental data, an artificial neural network (ANN) model was developed as a tool to predict the possibility of arcing for selected parameter combinations. This model can help avoid the parameter combinations which can lead to arcing during actual machining using EDM. The ANN model was validated by conducting validation experiments to ensure that it can work accurately as a predicting tool to know beforehand whether the selected parameters will lead to arcing during actual machining using EDM. Validation results show that the ANN model developed can predict arcing possibility accurately when the depth of machining is included as input variable for the model.  相似文献   

18.
基于改进BP网络的电火花加工工艺选择模型   总被引:2,自引:0,他引:2  
彭泽军  王宝瑞  陈辉 《中国机械工程》2005,16(18):1617-1621
提出了基于对数变换的数据预处理改进算法,测试表明效果较好。以加工面积、电极损耗比、表面粗糙度为输入参数,脉冲电流、脉冲宽度、脉冲间隙、放电间隙、伺服基准、伺服速度、加工速度为输出参数,提出了基于改进BP神经网络的电火花加工工艺选择模型。经过与实验数据的比较,该模型能真实反映机床的加工工艺规律,能实现在给定加工条件下进行电加工参数的自动选择。  相似文献   

19.
电火花线切割加工时放电加工状态决定着加工的质量和速度,电极丝和工件之间的放电电压是对放电加工过程进行实时检测的重要参数.以LabVIEW为开发平台,以放电电压为检测参数,构建了电火花线切割放电加工状态识别仿真系统.该系统主要包括线切割放电状态识别模块、BP神经网络放电预测模块以及加工稳定性分析模块,对提高电火花线切割加工质量、加工效率及智能化加工有良好的效果.  相似文献   

20.
为最大限度减少热误差对多轴联动机床加工精度的影响,综合遗传算法全局收敛性和人工神经网络局部搜索快速性的优点,提出一种基于遗传算法优化BP网络隐层节点数及初始值的机床热误差建模方法。运用Matlab-GUI工具开发了具有通用性的交互式多轴机床热误差建模仿真系统,通过与传统的BP神经网络进行对比分析及试验论证,证明该模型预测精度更高、通用性强。  相似文献   

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