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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 相似文献
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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。 相似文献
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内燃机缸盖系统模式识别方法研究 总被引:3,自引:0,他引:3
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 相似文献
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基于人工神经网络的零件表面加工方案决策方法研究 总被引:5,自引:0,他引:5
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。 相似文献
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M.A. Karkoub A.H. Elkholy O.M. Al-hawaj 《The International Journal of Advanced Manufacturing Technology》2002,20(12):871-882
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 相似文献
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Babur Ozcelik Hasan Oktem Hasan Kurtaran 《The International Journal of Advanced Manufacturing Technology》2005,27(3-4):234-241
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. 相似文献
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Ali Azadeh Mohsen Moghaddam Pegah Geranmayeh Arash Naghavi 《The International Journal of Advanced Manufacturing Technology》2010,50(5-8):699-715
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. 相似文献
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Simranpreet Singh Gill Jagdev Singh 《The International Journal of Advanced Manufacturing Technology》2013,69(9-12):2001-2009
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. 相似文献
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BP神经网络在摩擦学设计计算中的应用 总被引:2,自引:0,他引:2
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。 相似文献
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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。 相似文献
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基于人工神经网络的磨料水射流切割加工模型 总被引:3,自引:2,他引:3
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。 相似文献