现代制造工程 ›› 2017, Vol. 436 ›› Issue (1): 76-81.doi: 10.16731/j.cnki.1671-3133.2017.01.015

• CAD/CAE/CAPP/CAM • 上一篇    下一篇

产品全生命周期过程知识树导航重用研究

冯全华1, 张东民1, 盛育东1, 张宏2   

  1. 1 上海应用技术大学机械工程学院,上海 201418;
    2 上海徐工工程机械有限公司,上海 201412
  • 收稿日期:2015-08-12 出版日期:2017-01-20 发布日期:2018-01-10
  • 作者简介:冯全华,硕士研究生,主要研究方向为产品数字化设计制造。张东民,教授,工学博士,主要研究方向为数字化设计制造、基于知识的设计等,已发表论文四十多篇。盛育东,硕士研究生,主要研究方向为制造过程数字仿真技术。E_mail:fengquanhua1028@163.com
  • 基金资助:
    上海市教委科研创新重点项目(12ZZ190)

Research on navigate and reuse of whole life cycle process knowledge

Feng Quanhua1, Zhang Dongmin1, Sheng Yudong1, Zhang Hong2   

  1. 1 School of Mechanical Engineering,Shanghai Institute of Technology,Shanghai 201418,China;
    2 Shanghai Xugong Construction Machinery Co.Ltd.,Shanghai 201412,China
  • Received:2015-08-12 Online:2017-01-20 Published:2018-01-10

摘要: 复杂机械产品的开发具有知识密集型的特点,知识重用策略在开发中起着重要作用。对机械产品全生命周期中容易忽略的过程知识进行研究,面向产品全生命周期和自身结构,构建产品过程知识本体,将大粒度的本体知识细化成含有知识节点和知识点的知识树。对知识树映射的过程知识进行建模,并通过知识树导航和关键词检索重用产品全生命周期过程知识,满足了不同技术层次开发者的需求。以摊铺机为例,开发了基于知识树导航的摊铺机全生命周期过程知识重用系统,验证了方法的可行性。

关键词: 过程知识, 知识建模, 知识重用, 知识树导航, 关键词检索

Abstract: The development of complex mechanical products have the characteristics of knowledge intensive,knowledge reuse plays an important role in the development.Researched on the easily ignored process knowledge in the whole life cycle of mechanical products,buildging process knowledge ontology based on the whole life cycle and structure of the product,to refined the large-grained ontology to knowledge tree which contains knowledge nodes and knowledge points.making the design process knowledge to a model which is corresponded to knowledge tree,then reuse the whole life cycle design process knowledge by knowledge tree navigation and keywords retrieve which meet the needs of different level of developer.Taking the grab for example,developed the process knowledge reuse system of the whole life cycle based on knowledge tree navigation,prove that the method is validated.

Key words: process knowledge, knowledge modeling, knowledge reuse, knowledge tree navigation, keywords retrieve

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