基于绿色特征的方案设计与评价

发布时间：2018-11-16 15:35

【摘要】：在人类物质文明飞速发展的同时,对资源、能源的过度消耗和对环境的无偿利用已经造成资源短缺、环境污染等一系列问题。对于产品来说,要从根本上解决环境问题,必须实施源头控制,将污染消除在生产过程之初,即方案设计阶段。因此,在产品方案设计的早期阶段,如何对环境有重要影响的绿色信息进行有效集成,对产品生命周期资源、能源、碳排放等环境属性做出正确评价,进而获得绿色设计方案,是提高产品环境性能的关键。常规的产品方案设计主要是从用户需求出发,利用QFD方法将用户需求转化为方案设计参数以及技术特征,对产品方案进行约束,主要关注产品零部件的低层几何信息和拓扑信息、原理信息、功能信息、基本的装配信息及可制造信息等,而没有详细的形状、尺寸、精度、制造和装配信息,信息量少且不确定程度很高,难以利用已有的信息完成产品的环境影响评价,进而优化产品方案。并且,由于方案设计师环境知识的匮乏,难以从严重不完整且不确定的设计参数中提取出绿色信息,并对其进行有效集成,进而完成产品生命周期评价。针对这些难题,本文提出了基于绿色特征的产品方案设计模型来支持产品在方案设计早期阶段的绿色设计与评价。 (1)针对方案设计阶段信息严重匮乏,难以提取完整的设计参数信息来支持生命周期评价过程的现状,本文建立了产品全生命周期模块化设计模型。首先,将产品按照功能性、绿色性等进行模块化设计；然后,根据需要,运用实例推理、约束、描述逻辑等模块选择方式,选取匹配模块,重组成符合要求的产品,生成产品模块化设计方案,从而获得绿色设计参数信息。 (2)建立了设计参数信息与绿色特征之间的双向信息映射模型和映射关系模型,将设计参数信息转换为对环境有重要影响的属性信息——绿色特征。针对映射关系中的关键技术进行了重点研究,并以某推土机变速箱行星架为例进行了验证。 (3)在课题组前期工作的基础上,进一步研究了基于绿色特征的快速生命周期评价(RLCA)及优化方法。分析了绿色特征中存在的不确定性信息,对其中的定量信息和定性信息分别采用蒙特卡罗法和模糊集理论进行处理,在此基础上完成了产品方案的快速生命周期评价；运用基于数理统计和基于神经网络的敏感性分析方法对评价结果进行敏感性分析,找出其中对评价结果影响比较大的因素,回溯到产品的设计参数信息,对方案进行优化。
[Abstract]:With the rapid development of human material civilization, the excessive consumption of resources and energy and the free use of the environment have caused a series of problems, such as the shortage of resources and environmental pollution. For the product, to solve the environmental problem fundamentally, it is necessary to carry out the source control and eliminate the pollution at the beginning of the production process, that is, the project design stage. Therefore, in the early stage of product design, how to integrate the green information which has important impact on the environment, and make a correct evaluation of the environmental attributes such as product life cycle resources, energy, carbon emissions, etc., and then obtain the green design scheme. It is the key to improve the environmental performance of products. The conventional product scheme design is mainly based on the user's requirements, and the QFD method is used to transform the user's requirements into the design parameters and technical features, which constrains the product scheme. Focusing on the lower level geometry and topology information, principle information, function information, basic assembly information and manufacturable information, etc., without detailed shape, size, precision, manufacture and assembly information, etc. Because of the small amount of information and the high degree of uncertainty, it is difficult to make use of the existing information to complete the environmental impact assessment of the product, and then optimize the product scheme. Moreover, due to the lack of environmental knowledge, it is difficult to extract green information from the seriously incomplete and uncertain design parameters, and integrate it effectively to complete the product life cycle evaluation. In order to solve these problems, a green feature based product design model is proposed to support the green design and evaluation in the early stage of the project design. (1) in view of the serious lack of information in the project design phase, it is difficult to extract complete design parameter information to support the life cycle evaluation process. In this paper, a product lifecycle modular design model is established. First of all, the product according to the functionality, green and other modular design; Then, according to the need, we select the matching module by using case-based reasoning, constraint, description logic and other module selection methods, reorganize into the product that meets the requirements, and generate the product modular design scheme, so as to obtain the green design parameter information. (2) the bidirectional information mapping model and the mapping relation model between the design parameter information and the green feature are established, and the design parameter information is transformed into the green feature, which has an important impact on the environment. The key technologies of mapping relation are studied and verified by an example of a bulldozer gearbox planetary frame. (3) based on the previous work of the research group, the fast life cycle evaluation (RLCA) and its optimization method based on green feature are further studied. The uncertain information in green feature is analyzed, and the quantitative and qualitative information are processed by Monte Carlo method and fuzzy set theory respectively. On this basis, the rapid life cycle evaluation of product scheme is completed. The sensitivity analysis method based on mathematical statistics and neural network is used to analyze the sensitivity of the evaluation results. The factors which have a great influence on the evaluation results are found out, and the design parameters are traced back to the design parameters of the products, and the scheme is optimized.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TB472

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