机械产品概念设计方案生成及评价方法研究
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摘要
概念设计是产品设计过程的起点,也是产品创新的关键阶段,它已成为产品开发创新的核心环节,也是提高产品质量、降低成本和增强企业竞争力的主要手段。从概念设计的过程来看,机械产品概念设计主要包括概念设计方案生成和概念设计方案评价两个主要阶段。随着对概念设计研究的深入,出现了一大批有关这两个方面的解决技术和解决工具,但概念设计过程的智能化一直是制约其发展的瓶颈。近年来,计算智能等智能优化技术得以长足发展,为尝试解决高度智能化、非线性的概念设计的方案生成与评价等关键问题奠定了理论基础。
本文在前人工作的基础上,从机械产品概念设计的一般过程出发,借助模糊逻辑、神经网络和遗传算法等计算智能技术及物理规划等智能优化技术对概念设计的方案生成,尤其是对不同条件下的概念设计方案评价方法进行了深入的研究,主要内容如下:
(1)对机械产品概念设计方案生成技术进行了研究。针对简单遗传算法收敛速度慢且容易陷入局部收敛等问题,根据已有文献研究,提出了一种改进的自适应遗传算法。在机械产品功能分析及原理方案组合形态学矩阵的基础上,构建基于改进的自适应遗传算法和BP神经网络的概念设计方案生成的混合优化模型,用改进的自适应遗传算法进行设计空间内解的搜索,并用BP神经网络实现改进遗传算法中解群体的适应度评价,从而避免传统形态学综合过程的组合爆炸和知识表达求解过程困难的问题。
(2)对评价指标可以量化时的概念设计方案评价和决策问题进行了研究和探讨。针对传统决策矩阵法中权值确定的主观性缺陷,采用Messac教授提出的线性物理规划多属性评价模型对评价指标可以量化时的概念设计方案进行评价和决策,在给定各设计目标满意程度区间的基础上,综合目标函数中使用的权值及各方案的总评价值由线性物理规划决策模型自动计算。
(3)对评价指标无法量化时的的概念设计方案评价和决策问题进行了研究。针对概念设计过程信息的不完全、不确定及不精确等特点,提出了一种基于RAOGA的模糊神经网络模型对评价指标值无法量化时的概念设计方案进行评价和决策。模糊神经网络模型运用前馈神经网络构造网络结构,利用基于RAOGA的模糊神经网络训练算法来确定神经网络的连接权值和阈值。模糊神经网络评价模型根据模糊集理论对评价指标进行模糊化处理,然后按照一定的推理规则进行模糊决策。
(4)对评价指标众多,而且评价指标间具有层次结构的概念设计方案评价和决策问题进行了研究。分析了基于线性物理规划的多属性评价模型和基于RAOGA的模糊神
Conceptual design is the beginning of product design process, and is also the key stage of product innovation, which has become the kernel step of product development and innovation. Conceptual design is the main measure to improve the quality and reduce the cost of products, and make the enterprise more competitive. According to the process of conceptual design, conceptual design of mechanical products mainly includes the following two phases: scheme generation and design candidate evaluation. With in-depth research on conceptual design, a number of tools and techniques have been proposed to address scheme generation and evaluation problems in conceptual design of mechanical products. But the intellectualization of conceptual design is always a bottleneck to restrict its development. In the recent years, intelligent optimization techniques have been developed rapidly, which attempt to set a theoretical basis to address the highly intelligentized and nonlinear scheme generation and evaluation problems of conceptual design.In this dissertation, based on the existing works, begin with the general process of conceptual design of mechanical products, scheme generation, especially design evaluation methods under different conditions are studied by using computational intelligence technique of fuzzy logic, neural network and genetic algorithm and intelligence optimization techniques of physical programming. The main research of this dissertation is as follows:(1) A scheme generation technique of conceptual design of mechanical products is studied. In view of the convergence speed of simple genetic algorithm is very low and the exploring process is easy to be trapped into local optimum, according to the known research, a modified adaptive genetic algorithm is proposed. On the basis of function analysis and morphological matrix of solution principles combination, a hybrid optimization model based on modified adaptive genetic algorithm and a BP neural network is constructed to perform the scheme generation of conceptual design process. An improved adaptive genetic algorithm is applied to explore schemes in the searching space of conceptual design, and a BP neural network is used to evaluate the fitness of the population in modified adaptive genetic algorithm. So the combination explosion and solving difficulty in knowledge representation of traditional morphological matrix can be avoided.(2) A design evaluation and decision-making problem when evaluation criteria can be quantified is investigated and discussed. To avoid the main drawback of typical decision
matrix that the decision maker must specify physically meaningless weights, the linear physical programming (LPP) multi-attribute evaluation model proposed by professor Messac was used to design evaluation and decision making in conceptual design on the condition that the evaluation criteria can be quantified. Based on the specified interval of preference regions of every criterion, the weights used in aggregate objective function and the evaluation values of every design candidate can be calculated by LPP model automatically.(3) A design evaluation and decision-making method when evaluation criteria can't be quantified is studied. The information managed in conceptual design is incomplete, uncertain and imprecise. In most cases, design criteria are difficult to be quantified and are only described with fuzzy linguistic variables. Based on this consideration, a ranking based adaptive evolutionary operator genetic algorithm (RAOGA)-based fuzzy neural network (FNN) model is developed to evaluate design candidates in conceptual design under the condition that design criteria can't be quantified. In FNN model, a feedforward neural network is used to construct the network structure and a RAOGA-based learning algorithm is adopted to adjust fuzzy weights and thresholds with fuzzy inputs and outputs of FNN. Based on fuzzy set theory, evaluation criteria are fuzzified first, and then fuzzy decision-making is performed according to certain reasoning rules by FNN model.(4) A multi-level design evaluation and decision-making problem is studied when too many evaluation criteria and hierarchy of criteria exist. The disadvantages of multi-attribute evaluation model based on LPP and RAOGA-based FNN evaluation model are analyzed when the problems with many hierarchical criteria are evaluated. On the basis of the work of Vanegas and Labib, a multi-level conceptual design evaluation model based on NFWA and fuzzy compromise decision making method is constructed. In the multi-level evaluation model, the group AHP based on fuzzy Delphi method is used to identify the fuzzy number of weights of all the criteria, the NFWA and fuzzy compromise decision making method is applied to calculate the overall desirability of each design alternative level by level. The use of fuzzy Delphi analytical hierarchical process can guarantee expert knowledge is reflected to decision making process and make decision making process more practical. Fuzzy compromise decision making method can calculate relative approach degrees between design candidates and fuzzy ideal solution, which can improve the resolving power of the evaluation model validly.
本文在前人工作的基础上,从机械产品概念设计的一般过程出发,借助模糊逻辑、神经网络和遗传算法等计算智能技术及物理规划等智能优化技术对概念设计的方案生成,尤其是对不同条件下的概念设计方案评价方法进行了深入的研究,主要内容如下:
(1)对机械产品概念设计方案生成技术进行了研究。针对简单遗传算法收敛速度慢且容易陷入局部收敛等问题,根据已有文献研究,提出了一种改进的自适应遗传算法。在机械产品功能分析及原理方案组合形态学矩阵的基础上,构建基于改进的自适应遗传算法和BP神经网络的概念设计方案生成的混合优化模型,用改进的自适应遗传算法进行设计空间内解的搜索,并用BP神经网络实现改进遗传算法中解群体的适应度评价,从而避免传统形态学综合过程的组合爆炸和知识表达求解过程困难的问题。
(2)对评价指标可以量化时的概念设计方案评价和决策问题进行了研究和探讨。针对传统决策矩阵法中权值确定的主观性缺陷,采用Messac教授提出的线性物理规划多属性评价模型对评价指标可以量化时的概念设计方案进行评价和决策,在给定各设计目标满意程度区间的基础上,综合目标函数中使用的权值及各方案的总评价值由线性物理规划决策模型自动计算。
(3)对评价指标无法量化时的的概念设计方案评价和决策问题进行了研究。针对概念设计过程信息的不完全、不确定及不精确等特点,提出了一种基于RAOGA的模糊神经网络模型对评价指标值无法量化时的概念设计方案进行评价和决策。模糊神经网络模型运用前馈神经网络构造网络结构,利用基于RAOGA的模糊神经网络训练算法来确定神经网络的连接权值和阈值。模糊神经网络评价模型根据模糊集理论对评价指标进行模糊化处理,然后按照一定的推理规则进行模糊决策。
(4)对评价指标众多,而且评价指标间具有层次结构的概念设计方案评价和决策问题进行了研究。分析了基于线性物理规划的多属性评价模型和基于RAOGA的模糊神
Conceptual design is the beginning of product design process, and is also the key stage of product innovation, which has become the kernel step of product development and innovation. Conceptual design is the main measure to improve the quality and reduce the cost of products, and make the enterprise more competitive. According to the process of conceptual design, conceptual design of mechanical products mainly includes the following two phases: scheme generation and design candidate evaluation. With in-depth research on conceptual design, a number of tools and techniques have been proposed to address scheme generation and evaluation problems in conceptual design of mechanical products. But the intellectualization of conceptual design is always a bottleneck to restrict its development. In the recent years, intelligent optimization techniques have been developed rapidly, which attempt to set a theoretical basis to address the highly intelligentized and nonlinear scheme generation and evaluation problems of conceptual design.In this dissertation, based on the existing works, begin with the general process of conceptual design of mechanical products, scheme generation, especially design evaluation methods under different conditions are studied by using computational intelligence technique of fuzzy logic, neural network and genetic algorithm and intelligence optimization techniques of physical programming. The main research of this dissertation is as follows:(1) A scheme generation technique of conceptual design of mechanical products is studied. In view of the convergence speed of simple genetic algorithm is very low and the exploring process is easy to be trapped into local optimum, according to the known research, a modified adaptive genetic algorithm is proposed. On the basis of function analysis and morphological matrix of solution principles combination, a hybrid optimization model based on modified adaptive genetic algorithm and a BP neural network is constructed to perform the scheme generation of conceptual design process. An improved adaptive genetic algorithm is applied to explore schemes in the searching space of conceptual design, and a BP neural network is used to evaluate the fitness of the population in modified adaptive genetic algorithm. So the combination explosion and solving difficulty in knowledge representation of traditional morphological matrix can be avoided.(2) A design evaluation and decision-making problem when evaluation criteria can be quantified is investigated and discussed. To avoid the main drawback of typical decision
matrix that the decision maker must specify physically meaningless weights, the linear physical programming (LPP) multi-attribute evaluation model proposed by professor Messac was used to design evaluation and decision making in conceptual design on the condition that the evaluation criteria can be quantified. Based on the specified interval of preference regions of every criterion, the weights used in aggregate objective function and the evaluation values of every design candidate can be calculated by LPP model automatically.(3) A design evaluation and decision-making method when evaluation criteria can't be quantified is studied. The information managed in conceptual design is incomplete, uncertain and imprecise. In most cases, design criteria are difficult to be quantified and are only described with fuzzy linguistic variables. Based on this consideration, a ranking based adaptive evolutionary operator genetic algorithm (RAOGA)-based fuzzy neural network (FNN) model is developed to evaluate design candidates in conceptual design under the condition that design criteria can't be quantified. In FNN model, a feedforward neural network is used to construct the network structure and a RAOGA-based learning algorithm is adopted to adjust fuzzy weights and thresholds with fuzzy inputs and outputs of FNN. Based on fuzzy set theory, evaluation criteria are fuzzified first, and then fuzzy decision-making is performed according to certain reasoning rules by FNN model.(4) A multi-level design evaluation and decision-making problem is studied when too many evaluation criteria and hierarchy of criteria exist. The disadvantages of multi-attribute evaluation model based on LPP and RAOGA-based FNN evaluation model are analyzed when the problems with many hierarchical criteria are evaluated. On the basis of the work of Vanegas and Labib, a multi-level conceptual design evaluation model based on NFWA and fuzzy compromise decision making method is constructed. In the multi-level evaluation model, the group AHP based on fuzzy Delphi method is used to identify the fuzzy number of weights of all the criteria, the NFWA and fuzzy compromise decision making method is applied to calculate the overall desirability of each design alternative level by level. The use of fuzzy Delphi analytical hierarchical process can guarantee expert knowledge is reflected to decision making process and make decision making process more practical. Fuzzy compromise decision making method can calculate relative approach degrees between design candidates and fuzzy ideal solution, which can improve the resolving power of the evaluation model validly.
引文
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