数控电火花线切割加工参数优选的试验研究

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。     

气液双介质电火花线切割精加工研究

往复走丝电火花线切割采用大气介质代替传统的液体介质，对Cr12MoV模具钢进行电火花线切割精加工，较液体介质可获得更高的表面加工质量，但也存在电蚀表面碳化、加工不稳定、电极丝热疲劳损伤大等问题。为解决上述问题，尝试采用气液双介质对模具钢进行电火花线切割精加工实验研究。结果表明，在相同电参数条件下，选择合适的进给速度、切削深度、运丝速度和液膜厚度，气液双介质中加工表面质量较液中明显提高，电极丝损耗较气中明显降低。     

线切割加工参数对工艺指标的影响分析及合理选择

电参数和非电参数是影响线切割加工工艺指标的两个重要方面，通过对DK7732型精密数控中走丝线切割机床的应用和研究，经过多次加工试验，得到了工艺试验数据，结合试验数据分析了工艺指标的影响因素，为实验教学和产品加工提供了科学依据。     

数控快走丝电火花线切割加工工艺方法研究

通过对数控快走丝电火花线切割加工中如何加工穿丝孔、装夹工件、编制程序、选择电参数、使用工作液、应用多次切割工艺方法以及诊断加工故障等工艺方法进行研究。形成了一套完整的工艺方法，效果良好。     

钛合金Ti-6Al-4V的电火花线切割参数试验研究

为了研究钛合金电火花线切割加工中影响加工速度与加工材料表面粗糙度的电参数因素,以快走丝电火花线切割机床的电参数为控变因子研究各个电参数对钛合金加工后的表面质量和加工速度的影响规律。通过单因素试验,正交试验的极差分析,研究电火花线切割各电参数对其性能指标的影响,试验结果表明:材料去除率与表面粗糙度是一对相互矛盾指标,各电参数对于这两项指标都有不同程度的影响。该研究旨在为优化钛合金的加工工艺提供参考。     

快走丝电火花线切割大厚度工件的电参数设置研究

电参数的选择是快走丝电火花线切割大厚度工件的重要内容,通过对脉冲宽度、占空比、加工电流和进给速度采用控制单因素变量法进行切割实验,揭示了快走丝电火花线切割大厚度工件的电参数选择原则,脉宽时间和占空比应选择偏小,在保持稳定加工的前提下,加工了电流和进给速度选择偏大,以期为大厚度工件的快走丝电火花线切割提供工艺参考。     

快走丝线切割电极丝磨损分析及解决措施

针对线切割加工中非正常磨损和断丝现象,介绍了线切割加工的原理和线切割加工中影响电极丝使用寿命的主要因素。通过采取电极丝的选材,保证运丝机构精度,合理选择电参数,确保冷却系统工作稳定等有效途径来提高线切割电极丝的使用寿命,有利于保证线切割加工的工件质量、提高加工效率和机床的使用经济性。     

大厚度工件线切割加工实例

结结合大厚度工件加工实例,分析其加工中存在的难点以及产生原因,通过在机床调整,工作液、电极丝及电参数选择等方面采取适当的工艺措施来提高工件表面加工质量,降低断丝几率.     

复合运丝型电火花线切割加工参数分析与研究

提出了一种新型电火花线切割机床,即电极丝作往复直线运动的同时还绕自身轴线高速旋转的复合运丝型线切割机床。介绍了该类机床与其他线切割机床加工的基本工艺指标。通过与高速走丝电火花线切割机床比较实验,分析了脉冲宽度、脉冲间隔、脉冲峰值电流等电参数对加工工艺指标的影响,实验表明这种独特的复合运丝方式在降低表面粗糙度、提高加工精度等方面较传统运丝方式具有较大的优越性,且机床结构较为简单,对于各种工艺参数和电参数具有更加广泛的适用性,具有进一步研究和推广价值。     

电极丝损耗或断丝的预防对策

数控电火花线切割加工中 ,电极丝的损耗或断丝严重影响其连续自动操作的进行。尤其是在高速走丝电火花线切割中 ,因电极丝在加工中的反复使用 ,随着电极丝损耗的增加 ,切缝越来越窄 ,不仅会使加工面的尺寸误差增大 ;而且一旦在加工中发生断丝 ,加工必须重新开始 ,这不仅花费较多工时 ,而且影响加工的表面质量。电火花线切割加工中引起电极丝过量损耗断丝的原因有多方面 ,如电脉冲的稳定性、工艺参数的合理性、机械传动精度的高低、工件材料切割性能的优劣、切割路线与夹持方式的正确与否等 ,这些对于电极丝的使用寿命均有一定的影响。针对电…     

A novel method for estimating the track-soil parameters based on Kalman and improved strong tracking filters

A tracked vehicle has been widely used in exploring unknown environments and military fields. In current methods for suiting soil conditions, soil parameters need to be given and the traction performance cannot always be satisfied on soft soil. To solve the problem, it is essential to estimate track-soil parameters in real-time. Therefore, a detailed mathematical model is proposed for the first time. Furthermore, a novel algorithm which is composed of Kalman filter (KF) and improved strong tracking filter (STF) is developed for online track-soil estimation and named as KF–ISTF. By this method, the KF is used to estimate slip parameters, and the ISTF is used to estimate motion states. Then the key soil parameters can be estimated by using a suitable soil model. The experimental results show that equipped with the estimation algorithm, the proposed model can be used to estimate the track-soil parameters, and make the traction performance satisfied with soil conditions.     

一种新型气候参数智能模拟系统

文章介绍了一种气候参数智能模拟系统。它除具有普通气候模拟系统的模拟设定值功能外,还可以提前采集气候参数并将参数长期保存于非易失性数据存储器DS1220中,等到需要时再将保存的气候参数再现出来。该系统实现了数据的智能采集和处理。避免了从键盘输入参数,简化了硬件电路。     

Distributed parameter model updating using the Karhunen–Loève expansion

Discrepancies between experimentally measured data and computational predictions are unavoidable for complex engineering dynamical systems. To reduce this gap, model updating methods have been developed over the past three decades. Current methods for model updating often use discrete parameters, such as thickness or joint stiffness, for model updating. However, there are many parameters in a numerical model which are spatially distributed in nature. Such parameters include, but are not limited to, thickness, Poisson's ratio, Young's modulus, density and damping. In this paper a novel approach is proposed which takes account of the distributed nature of the parameters to be updated, by expressing the parameters as spatially correlated random fields. Based on this assumption, the random fields corresponding to the parameters to be updated have been expanded in a spectral decomposition known as the Karhunen–Loève (KL) expansion. Using the KL expansion, the mass and stiffness matrices are expanded in series in terms of discrete parameters. These parameters in turn are obtained using a sensitivity based optimization approach. A numerical example involving a beam with distributed updating parameters is used to illustrate this new idea.     

Feature selection techniques and comparative studies for large-scale manufacturing processes

In order to efficiently and effectively control an overall process in the process industry, a few important parameters should be identified from high-dimensional, non-linear, and correlated data. Feature selection techniques can be employed to extract a subset of process parameters relevant to product quality. The performance of these techniques depends on the precision of the prediction model formulated to quantify the relationship between the process parameters and the quality characteristics. Although the neural network-based partial least squares (NNPLS) method has been proven to be effective in prediction models for the aforementioned industrial process data, feature selection techniques appropriate for NNPLS models have yet to appear. Here, several techniques for scoring the relevance of process parameters to product quality are proposed and validated by applying three datasets. These experiments show that the proposed techniques can discriminate relevant process parameters from irrelevant ones.     

基于模具CAD的孔系自动识别

根据模具加工中的具体要求,采用VB对AutoCAD进行二次开发,对CAD模具设计图形中的孔进行参数提取、统计,分析各孔的直径、圆心位置坐标、个数等参数,把各孔编号并标注到图上。所有的参数和编号以表格的形式显示出来,进而形成文本文件保存下来以备加工时用,各孔的圆心位置坐标应能根据坐标系统的改变而相应改变。     

Feature selection techniques and comparative studies for large-scale manufacturing processes

In order to efficiently and effectively control an overall process in the process industry, a few important parameters should be identified from high-dimensional, non-linear, and correlated data. Feature selection techniques can be employed to extract a subset of process parameters relevant to product quality. The performance of these techniques depends on the precision of the prediction model formulated to quantify the relationship between the process parameters and the quality characteristics. Although the neural network-based partial least squares (NNPLS) method has been proven to be effective in prediction models for the aforementioned industrial process data, feature selection techniques appropriate for NNPLS models have yet to appear. Here, several techniques for scoring the relevance of process parameters to product quality are proposed and validated by applying three datasets. These experiments show that the proposed techniques can discriminate relevant process parameters from irrelevant ones.     

PARAMETER IDENTIFIABILITY IN FINITE ELEMENT MODEL ERROR LOCALISATION

A fundamental question in finite element model updating and error localisation is whether sufficient identifiability of model parameters is at hand for a given set of test data. Under certain conditions, the dynamic properties (to be compared with test data) of a structural model, may change similarly when a certain model parameter or a combination of other parameters are modified. Since low confidence in identified parameters can also be expected for marginally identifiable systems, due to the omnipresent noise when real test data are used, one should seek such states so as to avoid them. Should the problem lack identifiability, then before a meaningful error localisation can be made; either complementary test data have to be added or new parameters chosen for the model. The latter is studied in this paper. An index, the orthogonality/colinearity index, was developed to facilitate finding the best way to reduce the number of parameters when there is low identifiability The use of the index is demonstrated on a six-degree-of-freedom system in a numerical example. The example shows that error localisation or model updating using a parameterisation which has insufficient parameter identifiability is pointless.     

大宽厚比薄壁异型材挤压多工艺参数优化研究

型材挤压多种工艺参数的优化设计是一个组合优化问题,难以用传统数学优化方法解决。在应用型材挤压CAD/CAE技术建立型材挤压CAD模型,并对其成形过程及其参数变化规律进行CAE仿真的基础上,采用基于正交试验、人工神经网络和遗传算法的型材挤压多工艺参数计算机辅助优化技术建立型材挤压多工艺参数与挤压质量间的关系映射模型,并预测不同参数值搭配对挤压质量的影响,进而确定多工艺参数最优解。试验证明取得了良好的效果。     

关节型工业机器人工作空间及结构尺寸参数的一种确定方法

提出了一种适用于工程设计的确定关节型工业机器人工作空间及结构尺寸参数的简易而又实用的方法。根据机器人工作空间主视图边界曲线上特定的三点座标值，利用数值计算方法就可以确定机器人的结构尺寸参数（大小臂长度Ｌ１和Ｌ２的值，及其旋转角θ１和θ２的极限值），再根据这些结构尺寸参数确定机器人的工作空间。另外依此编制了相应的软件Ｒｏｂｓｐａｃｅ，只要输入三点的座标值及机器人机座的高度和机座旋转角度，便能确定机器人结构尺寸参数及其工作空间，并能显示、打印结构尺寸参数结果及工作空间边界曲线。