Platform architecture concept for the composition of collective cloud manufacturing

Strljic, Matthias Milan; Brovkina, Daniella; Korb, Tobias; Riedel, Oliver; Lechler, Armin

doi:10.1109/m2vip.2018.8600839

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…In terms of the platform's architecture, Simeone et al [3] proposed an intelligent platform to improve resources efficiency. Aiming at realizing a continuous production chain for collective cloud manufacturing, Strljic et al [5] developed a system architecture that considered the dynamic changes of Industry 4.0. Li et al [6] proposed a distributed peer-to-peer network architecture based on block chain technologies to improve the security and scalability of cloud manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Research on cloud manufacturing platform for small and medium-sized enterprises

Chen

Liu

et al. 2020

MATEC Web Conf.

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Keywords: Cloud manufacturing, Modeling and standardization of manufacturing services, Supply and demand matching, Remote manufacturing service monitoring.Abstract. With the adjustment of China's economic structure, it is essential for manufacturing enterprises, especially SMEs, to improve their core competitiveness. Based on the service mode of Cloud manufacturing, this paper designs and develops a cloud manufacturing platform for SMEs. Firstly, according to the characteristics of cloud manufacturing, the overall framework of the platform is determined, and the corresponding function tree is refined. Then, the key technologies are studied, including the modeling and standardization of manufacturing services, the matching of task, the execution, the online monitoring of service, and the evaluation of post-service. The functions of service access, service matching, outsourcing task management, remote monitoring, and evaluation of tasks are realized. Finally, the platform is applied in the mold manufacturing industry, and an example analysis is carried out to verify the effectiveness of the proposed platform.

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Section: Introductionmentioning

confidence: 99%