机械运动方案智能CAD系统模型研究

在分析制约机械运动方案智能 CA D发展因素的基础上 ,提出人机协作型机械运动方案智能 CAD设计模式 ,并根据运动方案设计过程中不同设计层次具有的设计特征 ,建立了面向对象的机械运动方案智能CAD系统设计模型 ,模型中每一个对象都有自己独立的设计模式和方法 ,由此使机械运动方案智能 CAD系统的扩展和实现成为可能 ,简化了系统程序的设计、维护和管理     

智能化电火花加工CAPP系统的研究

针对电火花加工工艺的特点,采用专家系统与人工神经网络相给合实现电火花加工智能化CAPP系统——I-EDM-CAPP系统。建立了基于规则的简易Petri网SPN模型以实现加工方法的智能选取;采用复合神经网络（CNN）实现电参数学习模型,从而能够准确地选择并合理地扩展电加工工艺参数。     

面向机电产品设计的新型智能CAD系统

介绍将人工神经网络技术引入面向机电产品设计的专家系统,对结合人工神经网络与专家系统的技术优势,构建新型智能CAD系统以及将该系统应用于机电产品设计进行了深入探讨。     

基于智能计算的齿轮CAD系统

为了尽可能减少齿轮设计中不确定因素造成的问题，提出了一种基于智能计算的齿轮CAD系统。本系统以材料选择专家系统为核心，利用智能计算中模糊逻辑和人工神经网络理论分别选出最佳材料类型和最优材料。通过迭代算法结合人工神经网络自动生成了齿轮各个参数，并实现了齿轮的优化设计。Delphi结合SQL Sever构建了系统的知识库，并提供了友好的用户设计环境。利用UG二次开发工具输出了齿轮的三维模型。     

基于CBR和RST的智能冲模CAD系统研究

冲模设计是一个复杂的过程，严重依赖于设计者的经验，如何提高冲模设计智能化程度一直是冲模CAD领域的研究重点。本文在分析冲模的实际设计过程和智能CAD系统基础上，建立了一个基于IDEF0的；中模CAD系统功能模型，设计实现了一个基于实例推理和粗糙集理论的智能冲模CAD原型系统，并对智能冲模CAD中实例表达、实例检索、实例评价与修改等关键技术进行了研究。使用该系统能提高设计质量和效率，缩短开发周期，降低冲模生产成本。     

内燃机缸盖系统模式识别方法研究

根据往复式内燃机结构和工作特点 ,对内燃机缸盖系统模式的识别方法进行了研究。根据激励与内燃机相位的对应关系 ,提出了基于相位相关的模式分解方法 ,用子模式集合描述缸盖系统模式。对人工神经网络技术用于缸盖系统模式识别进行的研究表明 ,人工神经网络技术对内燃机缸盖系统模式识别具有较强的分类表达和诊断能力     

关联设计技术及其在内燃机CAD系统中的应用

关联设计是为了克服传统CAD系统的缺点而发展起来的一种CAD新技术，它着重强调产品模型建模与修改的方便性，强调产品模型内部各子部分之间的联系，实现整机产品的关联设计与联动修改。通过深入研究内燃机运动系统结构特征，获得了产品结构设计知识，提出了关联设计在内燃机CAD系统设计中应用的必要性。结合Pro/NGINEER及其二次开发工具Pro/ToolKit，阐述了关联设计方法及其实现机制，并以此为基础开发了内燃机CAD系统。     

集成化智能模具CAD系统研究

介绍了集成化智能模具CAD系统的体系结构，包括基于特征的模具CAD集成信息模型，和基于黑板推理的智能决策模型，并以实例说明该模具CAD系统的集成和智能推理工作过程。     

面向CAD/CAE集成的注塑模冷却系统特征识别

提出了一种基于图的冷却系统特征识别方法,并将该方法应用于注塑模CAD/CAE集成。针对注塑模冷却系统的结构特点,定义了一组推断规则。对注塑模三维CAD模型中包含冷却系统的模板建立面边图,利用推断规则识别出面边图中的冷却系统特征子图,进而对识别出的冷却回路数据进行提取和优化,并转换成CAE分析所需的格式。以此为基础,在UG平台上开发了注塑模冷却系统CAD/CAE集成模块,实现了冷却系统模型从CAD到CAE系统的自动重构,避免了不同系统之间的重复建模。     

基于部件的分层智能诊断系统研究

为提高智能故障诊断系统对复杂机械设备的诊断能力,提出基于部件的人工神经网络专家系统诊断模型,根据机组结构模型,采用面向对象的知识库构建方法,集成了人工神经网络诊断技术和基于规则的诊断技术,提高了诊断的效率。     

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。     

基于人工神经网络的零件表面加工方案决策方法研究

零件表面加工方案的选择是一个多因素约束的逻辑推理过程，需要大量工艺知识支持，基于规则和基于框架的知识表达方法，在知识量大的情况下，存在效率低和推理效率低的缺陷，本文建立了基于人工神经网络的零件形面加工方案决策模型，设计了学习样本，用BP算法对网络进行训练，把一系列推理规则转化为网络权值，应用训练好的网络生成零件形面加工方法甸。     

Modelling Deformation of Hydroformed Circular Plates Using Neural Networks

The process of applying fluid pressure to form metal sheets into desired shapes is widely used in the industry and is known as hydroforming. Similar to most other metal forming processes, hydroforming leads to non-homogeneous plastic deformation of the workpiece. Predicting the amount of deformation caused by any sheet metal forming process leads to better products. In this paper, a model is developed to predict the amount of deformation caused by hydroforming using an artificial intelligence technique known as neural networks. The data used to design the neural network model is collected from an apparatus that was designed and built in our laboratory. The neural network model has a feedforward architecture and uses Powell’s optimisation techniques in the training process. Single- and two-hidden-layer feedforward neural network models are used to capture the nonlinear correlations between the input and output data. The neural network model was able to predict the centre deflection, the thickness variation, and the deformed shape of circular plate specimens with good accuracy. ID="A1"Correspondance and offprint requests to: Dr M. Karkoub, Mechanical and Industrial Engineering Department, College of Engineering and Petroleum, Kuwait University, PO Box 5969, Safat 13060, Kuwait     

粗集理论在污水参数软测量中的应用研究

用粗糙集理论约简属性,消除冗余信息后建立了污泥体积指数的神经网络软测量模型.用某城市污水厂实际水质参数进行仿真实验.仿真结果表明,与未采用粗糙集进行预处理的模型相比,应用该模型不仅测量值的误差更小,而且输入参数从9个降至4个,大大降低了输入数据的维数,减少了神经网络的训练时间及训练步数,有利于软测量模型的实用化.     

Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm

In this study, optimum cutting parameters of Inconel 718 are determined to enable minimum surface roughness under the constraints of roughness and material removal rate. In doing this, advantages of statistical experimental design technique, experimental measurements, artificial neural network and genetic optimization method are exploited in an integrated manner. Cutting experiments are designed based on statistical three-level full factorial experimental design technique. A predictive model for surface roughness is created using a feed forward artificial neural network exploiting experimental data. Neural network model and analytical definition of material removal rate are employed in the construction of optimization problem. The optimization problem was solved by an effective genetic algorithm for variety of constraint limits. Additional experiments have been conducted to compare optimum values and their corresponding roughness and material removal rate values predicted from the genetic algorithm. Generally a good correlation is observed between the predicted optimum and the experimental measurements. The neural network model coupled with genetic algorithm can be effectively utilized to find the best or optimum cutting parameter values for a specific cutting condition in end milling Inconel 718.     

A flexible artificial neural network–fuzzy simulation algorithm for scheduling a flow shop with multiple processors

One of the most popular approaches for scheduling manufacturing systems is dispatching rules. Different types of dispatching rules exist, but none of them is known to be globally the best. A flexible artificial neural network–fuzzy simulation (FANN–FS) algorithm is presented in this study for solving the multiattribute combinatorial dispatching (MACD) decision problem. Artificial neural networks (ANNs) are one of the commonly used metaheuristics and are a proven tool for solving complex optimization problems. Hence, multilayered neural network metamodels and a fuzzy simulation using the α-cuts method were trained to provide a complex MACD problem. Fuzzy simulation is used to solve complex optimization problems to deal with imprecision and uncertainty. The proposed flexible algorithm is capable of modeling nonlinear, stochastic, and uncertain problems. It uses ANN simulation for crisp input data and fuzzy simulation for imprecise and uncertain input data. The solution quality is illustrated by two case studies from a multilayer ceramic capacitor manufacturing plant. The manufacturing lead times produced by the FANN–FS model turned out to be superior to conventional simulation models. This is the first study that introduces an intelligent and flexible approach for handling imprecision and nonlinearity of scheduling problems in flow shops with multiple processors.     

BP神经网络在摩擦学设计计算中的应用

用双隐层ＢＰ人工神经网络,建立了丝杆螺母副的磨损率与滑动速度关系的数学模型。该模型可用于准确地计算丝杆螺母副和蜗轮蜗杆副的磨损率,可十分方便地用于摩擦学程序设计。采用Ｌ－Ｍ规则进行神经网络学习训练可使网络收敛快,误差小。风络输出结果与实验结果比较有极好的吻合性。该神经网络为工程设计人员,在摩擦学设计时提供有效的计算工具。     

Artificial intelligent modeling to predict tensile strength of inertia friction-welded pipe joints

Adaptive neural network-based fuzzy inference system (ANFIS) is an artificial intelligent neuro-fuzzy technique used for modeling and control of ill-defined and uncertain systems. The present paper proposes this novel technique of ANFIS to predict the tensile strength of inertia friction-welded tubular pipe joints with the aid of artificial neural network approach combined with the principle of fuzzy logic. The proposed model is multiple input–single output type of model which uses rotational speed and forge load as input signals. The set of rules has been generated directly from the experimental data using ANFIS. The performance of the proposed model is validated by comparing the predicted results with the actual practical results obtained by conducting the confirmation experiments. The application of χ ² test confirms that the values of tensile strength predicted by proposed ANFIS model are well in agreement with the experimental values at 0.1 % level of significance. The proposed model can also be used as intelligent online adaptive control model for pipeline welding.     

基于遗传算法和神经网络的塔机结构动态优化设计

利用遗传算法和BP神经网络建立复杂结构系统动态优化的计算模型,该模型可代替系统原来的有限元模型,用于振动系统的快速重分析。首先对塔式起重机结构系统进行模态分析及谐响应动力学分析,找出对结构动态特性影响最大的模态频率,再利用灵敏度分析,确定对动态特性较敏感的设计变量作为神经网络的输入变量,并利用正交试验法确定神经网络训练样本,用有限元模型计算出样本点数据,建立反映结构振动特性的人工神经网络模型,最后利用遗传算法对所建立的神经网络模型寻优,得到使结构动态性能最优的设计参数。     

基于人工神经网络的磨料水射流切割加工模型

作为一种冷态加工新工艺 ,磨料水射流 (AWJ)以其独特的优点得到广泛应用 ,但由于高速液固两相流本身的特性 ,AWJ切割加工是一个受多参数影响的复杂随机过程 ,很难建立一个适当的机理模型。基于人工神经网络理论 ,本文建立了切割厚度与主要工艺参数间的AWJ切割加工预测模型 ,模型预测结果与实验值及Zeng的经验模型进行了比较 ,该网络模型能可靠、准确地映射出AWJ的加工规律 ,可应用于AWJ切割加工过程的参数优化选择、加工规律计算机仿真及智能化控制中。