Reconfigurable Smart Factory for Drug Packing in Healthcare Industry 4.0

被引:94
作者
Wan, Jiafu [1 ]
Tang, Shenglong [1 ]
Li, Di [1 ]
Imran, Muhammad [2 ]
Zhang, Chunhua [1 ]
Liu, Chengliang [3 ]
Pang, Zhibo [4 ]
机构
[1] South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510641, Guangdong, Peoples R China
[2] King Saud Univ, Coll Comp & Informat Sci, Riyadh 11451, Saudi Arabia
[3] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200000, Peoples R China
[4] ABB Corp Res, S-72178 Vasteras, Sweden
关键词
Cyber-physical systems (CPS); healthcare industry 4.0; IEC; 61499; ontology; reconfiguration; smart factory; DISTRIBUTED AUTOMATION; IEC; 61499; MODEL;
D O I
10.1109/TII.2018.2843811
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Industry 4.0, which exploits cyber-physical systems and represents digital transformation of manufacturing, is deeply affecting healthcare as well as other traditional production sector. To accommodate the increasing demand of agility, flexibility, and low cost in healthcare sector, a data-driven reconfigurable production mode of Smart Factory for pharmaceutical manufacturing is proposed in this paper. The architecture of the Smart Factory is consisted of three primary layers, namely perception layer, deployment layer, and executing layer. A Manufacturing's Semantics Ontology based knowledgebase is introduced in the perception layer, which is responsible for plan scheduling of pharmaceutical production. The reconfigurable plans are generated from the production demand of drugs as well as the information statement of low-level machine resources. To further functionality reconfiguration and low level controlling, the IEC 61499 standard is also introduced for functionality modeling and machine controlling. We verify the proposed method with an experiment of demand-based drug packing production, which reflects the feasibility and adequate flexibility of the proposed method.
引用
收藏
页码:507 / 516
页数:10
相关论文
共 34 条
  • [1] Abburu S., 2012, INT J COMPUT APPL, V57
  • [2] FAMILIAR: A domain-specific language for large scale management of feature models
    Acher, Mathieu
    Collet, Philippe
    Lahire, Philippe
    France, Robert B.
    [J]. SCIENCE OF COMPUTER PROGRAMMING, 2013, 78 (06) : 657 - 681
  • [3] Ontology-based reconfiguration agent for intelligent mechatronic systems in flexible manufacturing
    Alsafi, Yazen
    Vyatkin, Valeriy
    [J]. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2010, 26 (04) : 381 - 391
  • [4] [Anonymous], 2015, P 2015 IEEE WORLD C
  • [5] A View of Cloud Computing
    Armbrust, Michael
    Fox, Armando
    Griffith, Rean
    Joseph, Anthony D.
    Katz, Randy
    Konwinski, Andy
    Lee, Gunho
    Patterson, David
    Rabkin, Ariel
    Stoica, Ion
    Zaharia, Matei
    [J]. COMMUNICATIONS OF THE ACM, 2010, 53 (04) : 50 - 58
  • [6] Semantic matchmaker with precondition and effect matching using SWRL
    Bener, Ayse B.
    Ozadali, Volkan
    Ilhan, Erdem Savas
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (05) : 9371 - 9377
  • [7] Calvanese D, 2017, SEMANT WEB, V8, P471, DOI 10.3233/SW-160217
  • [8] Improvement of manufacturing operations at a pharmaceutical company A lean manufacturing approach
    Chowdary, Boppana V.
    George, Damian
    [J]. JOURNAL OF MANUFACTURING TECHNOLOGY MANAGEMENT, 2011, 23 (01) : 56 - 75
  • [9] Automatically Generated Layered Ontological Models for Semantic Analysis of Component-Based Control Systems
    Dai, Wenbin
    Dubinin, Victor N.
    Vyatkin, Valeriy
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2013, 9 (04) : 2124 - 2136
  • [10] Goncalves Rafael S., 2012, The Semantic Web. 11th International Semantic Web Conference (ISWC 2012). Proceedings, P82, DOI 10.1007/978-3-642-35176-1_6