方差分析法在鲁棒公差设计中的应用

目前公差优化设计主要在于降低加工成本,而很少考虑公差对制造加工的敏感度问题。为此,在公差设计中采用了方差分析法,通过对加工模拟所产生的一系列数据的分析,找出对封闭环有显著影响的设计变量,并提高该设计变量的公差要求,从而减小封闭环对加工变动的敏感度,实现面向质量的鲁棒公差设计。     

装配和制造综合的公差分配优化设计

面对航天产品的公差分配不合理的问题,提出综合考虑装配和制造两层面的公差分配方案。分析研究对象装配尺寸链的组成环情况,在零件制造的工序级别以制造成本-公差函数和质量损失函数为优化目标,以装配公差要求、工序间加工余量和工序的加工能力为约束条件,建立公差分配的多目标优化模型。采用基于Pareto机制的带精英策略的非支配排序遗传算法求解公差分配优化模型。以某航天企业电液伺服阀产品中衔铁组件的制造和装配为例,验证了模型的适用性和有效性。     

面向制造过程的装配公差模型研究

为了研究零件制造偏差对装配公差的影响,提出了一种面向制造过程的装配公差模型.基于小位移矢量方法建立零件表面公差模型,采用齐次坐标变换方法描述偏差累积,建立了工艺系统各组件的装配公差模型.在满足装配约束条件的前提下,考虑了装配零件的几何变动,利用统计法研究装配体的装配成功率.最后应用一实例对装配体进行了公差分析,验证了模型的合理性.该模型可以用于指导装配规划,避免不可装配性.     

制造成本最低的机械产品公差设计方法

在建立公差－－成本模型的基础上,提出了以制造成本最低为目标的机械产品零部件公差优化设计方法,并进行了实例分析计算。采用该方法在产品大批量生产条件下将会产生明显的经济效益。     

保证公差要求的装夹和加工顺序自动规划

提出了一个保证公差要求的装夹方案和特征加顺序自动规划方法。该方法根据机床的加工精度确定关键公差特征,以关键公差特征和唯一加工方法特征为基础,将零件的特征模型分解为相同装夹特征集合。该方法具有较快的规划与排序的速度,与设计系统紧密集成,并可在规划的过程中实现零件的整体可制造性评价功能。     

虚拟产品开发中的并行公差设计

并行公差设计能够有效解决VPD中的公差控制与公差分配问题，有利于VPD的全面集成以及面向加工和面向装配的实现。本文介绍了并行公差设计的内涵和特点，并提出了并行公差设计的系统结构。在此基础上开发的基于PDM的并行公差设计系统是虚拟开发平台的一个重要组成部分，已经通过863CIMS主题专家组的验收。     

面向成本的设计方法与技术研究

在分析产品成本相关领域研究的基础上，提出了面向成本的设计方法。根据面向成本的设计方法，在产品概念设计阶段，应用价值工程及ＤＦＡＭ的分析方法，实现产品结构优化。在产品详细设计阶段，通过建立包含制造加工，装配，检测等成本信息的产品模型，实现 技术经济性评价，根据评价结果，及时进行产品再设计，降低产品成本。     

关于再制造的公差优化模型研究

笔者通过将公差设计法引用到制造工程中,深入分析了再制造公差的设计特点和分配原则,并以再制造的最低生产总成本为目标,充分考虑了工程表面技术、产品性能、过程能力指标等因素。试图探索出一套以公差原理为基础的再制造公差优化数学模型,并将其与传统的再制造方案进行比较,从而证明该公差优化模型的实用性和有效性。     

上海光源储存环不锈钢真空室

铝合金和不锈钢都适合制造上海光源储存环的真空室,但各有特色,从而导致不同的加工和成本问题.实质上,不锈钢真空室可以看作是铝合金真空室的一个"内核".上海光源最终采用了不锈钢真空室,其主要理由是加工铝合金真空室时要用数控床切削掉约一半的材料,而加工壁薄不锈钢真空室几乎没有材料的损失.因此,经费大量节省和加工周期大大缩短.但结构复杂的薄壁不锈钢真空室的尺寸精度要做到和机加工铝合金真空室相当,难度极大.在研制了多个样机后,终于解决了成形片尺寸公差和焊接变形等难题,定型了工艺和工装,真空室的尺寸公差得到了控制.长度约3m的每段真空室的平面度和直线度的公差都小于1mm.真空室经900℃真空退火后,焊缝处的导磁率从2.5下降到1.02.真空预调试后真空室内达到5×10 -9Pa的极限真空.400m真空室现场安装后的位置公差都小于2mm.     

机械设计中零件一般公差的运用

零件的公差与配合的正确、合理选用是机械设计中的一项重要工作内容，它对促进互换性生产，提高产品质量，降低制造成本都具有重要意义。选用公差时应遵循三个基本选用原则，即基准制选用原则、公差等级选用原则和公差带及配合选用原则。对于机器零件在功能上无特殊要求的要素，设计中可选择一般公差，既简化设计制造，又降低生产成本，提高产品互换性。     

Cost-tolerance analysis model based on a neural networks method

The purpose of tolerance design in product components is to produce a product with the least manufacturing cost possible, while meeting all functional requirements of the product. The product designer and process planner must fully understand the process accuracy and manufacturing cost of all kinds of manufacturing process to perform a good process plan job. Usually, the cost-tolerance model is constructed by a linear or non-linear regression analysis based on the data of the cost-tolerance experiment and to derive the correlation curve between the two. Though these correlation curves can show the relationship between manufacturing cost and tolerance, a fitting error is inevitable. In particular, there is considerable discrepancy in terms of the non-experimental data. A cost-tolerance analysis model based on a neural networks method is proposed. The cost-tolerance experimental data are used to set the training sets to establish a cost-tolerance network. Three representation modes of the cost-tolerance relationship are presented. First, the cost-tolerance relationship is derived from the grid points setting by the required tolerance accuracy. Second, a reasonable manufacturing cost of an unknown cost-tolerance experimental pair can be derived by the simulation of a cost-tolerance network. Third, an inference model based on a network's output is proposed to express the scope of the cost variation of various tolerances by means of a cost band. Comparison is also made with the high-order polynomial power function and exponential function cost-tolerance curves adopted by Yeo et al . Analytical results prove that the application of the cost-tolerance analysis model based on neural networks yields better performance in controlling the average fitting error than all conventional fitting models. The representation model using a cost band can identify precisely the possible cost variation range and reduce the chances of error in the tolerance design and cost estimation. It can thus provide important references for tolerance designers and process planners.     

A proposal of cost-tolerance models directly collected from the manufacturing process

In this paper, the concept of cost-tolerance functions based on data directly obtained from the manufacturing process is proposed. Traditional models used in manufacturing field define cost-tolerance relationships from mathematical functions whose relationship with the process is not usually set in a clear way. In this work, proposed models allow to obtain cost-tolerance functions directly from parameters measured on manufacturing process. The paper defines some of these functions using statistical distributions derived from the population of manufactured parts. A general methodology to reach a cost-tolerance function from any distribution of the manufactured parts is proposed. Once functions are defined, an application example is presented. Finally, the main criteria for suitable appropriateness use of models are established.     

基于混合智能的刀具状态在线识别

提出了一个将小波包分析方法,模糊理论及人工神经网络技术相结合的智能刀具状态在线监测系统,系统利用小波包方法将声发射信号分解为不同频带的时间序列,从中抽取出与刀具切削状态紧密相关的序列信号的构方根值（RMS）作为信号特征值,为了表示刀具状态与特征值之间的关系提出了一个模糊神经网络模型,采用了自组织竞争学习与BP算法相结合的混合学习算法,可迅速,可靠地对刀具状态进行识别。     

Prediction and optimization of a ceramic casting process using a hierarchical hybrid system of neural networks and fuzzy logic

This paper is a case study that describes a hybrid system integrating fuzzy logic, neural networks and algorithmic optimization for use in the ceramics industry. A prediction module estimates two quality metrics of slip-cast pieces through the simultaneous execution of two neural networks. A process improvement algorithm optimizes controllable process settings using the neural network prediction module in the objective function. An expert system module contains a hierarchy of two fuzzy logic rule bases. The rule bases prescribe processing times customized to individual production lines given ambient conditions, mold characteristics and the neural network predictions. This paper demonstrates the applicability of newer computational techniques to a very traditional manufacturing process and the system has been implemented at a major US plant.     

Global optimization of a feature-based process sequence using GA and ANN techniques

Operation sequencing has been a key area of research and development for computer-aided process planning (CAPP). An optimal process sequence could largely increase the efficiency and decrease the cost of production. Genetic algorithms (GAs) are a technique for seeking to ‘breed’ good solutions to complex problems by survival of the fittest. Some attempts using GAs have been made on operation sequencing optimization, but few systems have intended to provide a globally optimized fitness function definition. In addition, most of the systems have a lack of adaptability or have an inability to learn. This paper presents an optimization strategy for process sequencing based on multi-objective fitness: minimum manufacturing cost, shortest manufacturing time and best satisfaction of manufacturing sequence rules. A hybrid approach is proposed to incorporate a genetic algorithm, neural network and analytical hierarchical process (AHP) for process sequencing. After a brief study of the current research, relevant issues of process planning are described. A globally optimized fitness function is then defined including the evaluation of manufacturing rules using AHP, calculation of cost and time and determination of relative weights using neural network techniques. The proposed GA-based process sequencing, the implementation and test results are discussed. Finally, conclusions and future work are summarized.     

Fuzzy neural network based yield prediction model for semiconductor manufacturing system

Accurate die yield prediction is very useful for improving yield, decreasing cost and maintaining good relationships with customers in the semiconductor manufacturing industry. To improve prediction accuracy of die yield, a novel fuzzy neural networks based yield prediction model is proposed in which the impact factors of yield and critical electrical test parameters are considered simultaneously and are taken as independent variables. The mapping between these independent variables and yield is constructed in the fuzzy neural network (FNN). The lineal regression between FNN-based yield predicting output and actual yield demonstrates the effectiveness of the proposed approach by historical experimental data of semiconductor fabrication line in Shanghai. The comparison experiment verifies the proposed yield prediction method improves on three traditional yield prediction methods with respect to prediction accuracy.     

运用模糊神经网络的汽车座椅舒适性评价

汽车座椅舒适性受到消费者的高度关注。由于复杂性和主观性等因素的影响,使得汽车座椅舒适度评价成为座椅制造企业和整车企业的一个技术难点。提出一种基于模糊神经网络的座椅舒适度评价模型,应用8个座椅压力分布参数和2个人体参数等对座椅舒适性进行综合评价。实例分析显示:与普通BP网络模型对比,模糊神经网络模型具有更好的预测效果。     

Fuzzy ridgelet neural network prediction model trained by improved particle swarm algorithm for maintenance decision of polypropylene plant

The proper maintenance plan should be made for ensuring the safety and reliability of polypropylene plant and improve economic benefits of petrochemical enterprise. To meet the requirement, a novel maintenance prediction model of polypropylene plant based on fuzzy theory, ridgelet an artificial neural network is constructed. The economy and reliability models of polypropylene plant maintenance are established through comprehensively considering the reliability and economy. The basic structure of fuzzy ridgelet neural network is designed, and the training algorithm is improved through combining the traditional particle swarm algorithm and bacterial foraging algorithm, and the corresponding algorithm flow is confirmed. Finally, prediction simulation analysis is carried out using a polypropylene plant as research object, and analysis results show that the fuzzy ridgelet neural network has best prediction effect, and the optimal maintenance plan can be confirmed to ensure security and reduce maintenance cost of polypropylene plant.     

基于模糊神经网络与主成分分析的磨削表面粗糙度在线预测

基于声发射和振动信号提出了一种模糊神经网络和主成分分析的表面粗糙度预测方法,以提高磨削过程中工件表面粗糙度识别的准确性。首先,采集磨削程中声发射与振动信号,提取相关时域特征、频域特征和小波包特征参数,利用主成分分析对特征量进行降维优化;然后,构建表面粗糙度模糊神经网络预测模型,将信号特征量与表面粗糙度作为模糊神经网络的输入和输出;最后,对模型进行训练,并对表面粗糙度预测精度进行验证。实验结果表明:通过主成分分析(PCA)方法对声发射和振动信号特征量进行降维得到5个主成分,以此建立的模糊神经网络表面粗糙度预测模型的效果精度可达到91%以上,与局部线性嵌入和多维标度法降维方法相比,PCA方法降维后的特征所含信息更优,预测准确度更高。