Study on machining service modes and resource selection strategies in cloud manufacturing

被引:35
作者
Cao, Yang [1 ]
Wang, Shilong [1 ]
Kang, Ling [1 ]
Li, Changsong [1 ]
Guo, Liang [2 ]
机构
[1] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China
[2] Southwest Petr Univ, Sch Mech Engn, Chengdu 610500, Sichuan, Peoples R China
关键词
Cloud manufacturing (CMfg); Machining service; Application mode; Prime service granularity; Web ontology language (OWL); Resource selection strategy; PLATFORM; QOS; COLLABORATION; ALGORITHM; EQUIPMENT;
D O I
10.1007/s00170-015-7222-z
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cloud manufacturing (CMfg) is a new service-oriented networked manufacturing paradigm inspired by cloud computing. It provides high-efficiency and intelligent manufacturing services by organizing isolated manufacturing resources in a collaborative manner. Since the proposition of this concept in 2010, relevant research has mainly focused on theoretical frameworks of the CMfg system. However, actual applications of the machining service, which is a key part in the CMfg service platform, are hardly ever studied. In order to explore a feasible machining service mode, prime granularities of machining services are analyzed based on the current state of the manufacturing industry. Then a novel part manufacturing service combined with working procedure manufacturing service (PMS + WPMS) prime collaboration mode is proposed, followed by research of machining resource integration methods. To facilitate prospective implementations, information models of machining services are constructed using Web ontology language (OWL). The prime collaboration mode is expanded to a complete CMfg machining service platform. Furthermore, a working procedure priority-based algorithm (WPPBA) is designed for resource selection in CMfg. Finally, simulation experiments based on actual manufacturing data are conducted, in which the test results demonstrate the feasibility of the proposed service mode and the effectiveness of WPPBA compared with genetic algorithm (GA) and particle swarm optimization (PSO). This research provides essential guidance for CMfg applications.
引用
收藏
页码:597 / 613
页数:17
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