摘要
为提升工业产品颜色、材料、表面处理(CMF)创新设计的效率和成功率,解决现有CMF创新设计过程中存在的设计知识获取效率低下、知识管理体系不完善、产品与设计一致性较弱等问题,立足于用户感性需求,引入数字孪生概念,借鉴数字孪生五维模型构建的理论与方法,针对性地构建基于数字孪生数据模式的工业产品CMF设计服务模型框架;从知识推送角度出发,以数据聚类重组理论为基础理解产品CMF语义库的组织结构和动态性,识别并预测产品CMF语义库中的意象方案与优选方案组合,作为析出知识供设计师规划设计活动。以一款虚拟现实眼镜的CMF创新设计为例,基于产品语义理论开发智能CMF设计引擎,利用析出知识简化设计复杂度并优化设计方案,有效验证了数字孪生驱动的工业产品CMF设计服务模型对设计的辅助作用。
To improve the efficiency and success rate of Color-Material-Finishing(CMF)innovative design on products for solving the defects existing in current CMF innovative design such as low efficiency in acquiring design knowledge,imperfect knowledge management system and weak consistency between product and design.Based on the users'perceptual demands,the concept of Digital Twin was introduced,and a targeted industrial product CMF design service model framework based on the digital twin data model was constructed by drawing on the theory and method of five-dimension digital twin model.From the perspective of knowledge push,the organization structure and dynamics of product CMF semantic library could be understood on the basis of data cluster recombination theory,and the image scheme and optimal scheme combination in product CMF semantic library could be identified and predicted,which was used as extracted knowledge for designers to plan and design activities.Taking CMF creative design of VR glasses as an example,an intelligent CMF design engine was developed based on the product semantic theory,and the extracted knowledge was used to simplify the design complexity and optimize the design scheme,thus the auxiliary function of CMF design service model of industrial products driven by digital twins was verified effectively to the design.
作者
李雪瑞
侯幸刚
杨梅
王璐瑶
王怡妍
李欣颖
LI Xuerui;HOU Xinggang;YANG Mei;WANG Luyao;WANG Yiyan;LI Xinying(College of Design and Arts,Shandong University of Science and Technology,Qingdao 266590,China)
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2021年第2期307-327,共21页
Computer Integrated Manufacturing Systems
基金
山东省重点研发计划(软科学项目)资助项目(2019RKB01395)
青岛市哲学社会科学规划资助项目(QDSKL1901141)
山东省艺术科学重点课题资助项目(QN201906118)。
