面向智能主模型的事物特性表扩展方法  

Extension Method of SML for Intelligent Master Model

作　　者：余聪[1] 张发平[1] 阎艳[1] 郝佳[1] 王国新[1] 吕武 YU Cong ZHANG Fa-ping YAN Yan HAO JiaI WANG Guo-xin LU Wu(School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China China Ordnance Industry No. 208 Research Institute, Beijing 102202, China)

机构地区：[1]北京理工大学机械与车辆学院,北京100081 [2]中国兵器工业第208研究所,北京102202

出　　处：《北京理工大学学报》2017年第7期727-732,共6页Transactions of Beijing Institute of Technology

基　　金：国家部委基础科研项目(A2220133001);国家部委基础科研项目(A1020131011)

摘　　要：针对常规事物特性表不能有效组织智能主模型中多视图、多层次产品信息和设计知识的问题,提出了事物特性表扩展方法.首先结合智能主模型信息需求对事物特性进行分析分类,然后从广度维扩展事物特性多视图内涵,以完整表达面向设计过程的产品特性;深度维扩展采用知识映射矩阵表达产品特性与设计知识的关联关系,为产品设计过程提供设计决策知识;粒度维扩展从产品、部件、零件等结构层次构建多层次事物特性表,满足智能主模型智能缩放产品结构,在早期设计阶段快速评估不同配置方案的需求.通过某枪管设计优化实例,验证了所提方法的有效性.A method of extending tabular layouts of article characteristics（SML） was proposed to solve the problem that conventional SML cannot organize multi-view, multi-level product information and design knowledge in intelligent master model（IMM）. Firstly, product article characteristics were multi-view clarified to meet the requirement of information expression in IMM. Then the content of article characteristics was extended to fully express multi-view information for the whole product design process in width dimension, and knowledge matrix was used to describe the association relationship between product article characteristics and design knowledge in depth dimension to provide designer with knowledge to make design decision, and the SML was constructed form product, component and part level in granularity dimension to enable intelligent master model intelligently scaling product structure so as to quickly evaluate different product configuration in early product development stages. Effectiveness of the presented method was verified by an example of barrel design and optimization.

关 键 词：事物特性表 扩展方法 多视图 知识矩阵 智能主模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]