Methods for the capture of manufacture best practice in product lifecycle management

被引:23
|
作者
Gunendran, A. G. [1 ]
Young, R. I. M. [1 ]
机构
[1] Univ Loughborough, Wolfson Sch Mech & Mfg Engn, Loughborough LE11 3TU, Leics, England
基金
英国经济与社会研究理事会; 英国工程与自然科学研究理事会;
关键词
manufacturing best practices; information and knowledge organisation; system design; UML; KNOWLEDGE-BASED SYSTEM; DESIGN; INFORMATION; FRAMEWORK; FEATURES;
D O I
10.1080/00207540903104210
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The capture of manufacturing best practice knowledge in product lifecycle management systems has significant potential to improve the quality of design decisions and minimise manufacturing problems during new product development. However, providing a reusable source of manufacturing best practice is difficult due to the complexity of the viewpoint relationships between products and the manufacturing processes and resources used to produce them. This paper discusses how best to organise manufacturing best practice knowledge, the relationships between elements of this knowledge plus their relationship to product information. The paper also explores the application of UML-2 as a system design tool which can model these relationships and hence support the reuse of system design models over time. The paper identifies a set of part family and feature libraries and, most significantly, the relationships between them, as a means of capturing best practice manufacturing knowledge and illustrates how these can be linked to manufacturing resource models and product information. Design for manufacture and machining best practice views are used in the paper to illustrate the concepts developed. An experimental knowledge based system has been developed and results generated using a power transmission shaft example.
引用
收藏
页码:5885 / 5904
页数:20
相关论文
共 50 条
  • [21] Big Data in product lifecycle management
    Li, Jingran
    Tao, Fei
    Cheng, Ying
    Zhao, Liangjin
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2015, 81 (1-4): : 667 - 684
  • [22] Big Data in product lifecycle management
    Jingran Li
    Fei Tao
    Ying Cheng
    Liangjin Zhao
    The International Journal of Advanced Manufacturing Technology, 2015, 81 : 667 - 684
  • [23] Information technology and product lifecycle management
    Thomas, V
    Neckel, W
    Wagner, S
    PROCEEDINGS OF THE 1999 IEEE INTERNATIONAL SYMPOSIUM ON ELECTRONICS AND THE ENVIRONMENT, ISEE - 1999, 1999, : 54 - 57
  • [24] Experience feedback in product lifecycle management
    Clermont, Philippe
    Kamsu-Foguem, Bernard
    COMPUTERS IN INDUSTRY, 2018, 95 : 1 - 14
  • [25] Ontology formalization of product semantics for Product Lifecycle Management
    Patil, Lalit
    Dutta, Debasish
    Sriram, Ram
    PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE 2005, VOL 3, PTS A AND B, 2005, : 809 - 816
  • [26] A product information modeling framework for product lifecycle management
    Sudarsan, R
    Fenves, SJ
    Sriram, RD
    Wang, F
    COMPUTER-AIDED DESIGN, 2005, 37 (13) : 1399 - 1411
  • [27] A framework for Product Lifecycle Management system
    Xu Xin-sheng
    Fang Shui-liang
    Gu Xin-jian
    Proceedings of the 2006 International Conference on Management Science & Engineering (13th), Vols 1-3, 2006, : 526 - 530
  • [28] Product lifecycle management for performance support
    Pham, DT
    Dimov, SS
    Setchi, RM
    Peat, B
    Soroka, AJ
    Brousseau, EB
    Huneiti, AM
    Lagos, N
    Noyvirt, AE
    Pasantonopoulos, C
    Tsaneva, DK
    Tang, Q
    JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING, 2004, 4 (04) : 305 - 315
  • [29] Artificial intelligence in product lifecycle management
    Lei Wang
    Zhengchao Liu
    Ang Liu
    Fei Tao
    The International Journal of Advanced Manufacturing Technology, 2021, 114 : 771 - 796
  • [30] Product Lifecycle Management Service System
    Wozniak, Dariusz
    Gohardani, Babak
    Majchrzak, Emil
    Hoti, Emiljana
    Urikova, Oksana
    ADVANCES IN INTELLIGENT NETWORKING AND COLLABORATIVE SYSTEMS, INCOS - 2019, 2020, 1035 : 525 - 533