A dynamic and static data based matching method for cloud 3D printing. (February 2020)

• Record Type: Journal Article
• Authors: Luo, Xiao; Zhang, Lin; Ren, Lei; Lali, Yuanjun
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 61(2020)
• Publication Date: 2020-02
• Subjects: 3D printing models -- Supply-demand matching -- Cloud manufacturing -- Model-based systems engineering -- Multi-source data integration model -- Capability indicator model; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2019.101858
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: The first study constructs a normative cloud 3D printing model by using the model-based system engineering method. The first study develops a dynamic and static data based matching method for cloud 3D printing. Grouping print resources can reduce the computational complexity of matching significantly. Fetching the real-time data of the printing resource model by the 3D printer dynamic data acquisition system. Abstract: 3D printing is widely used in such sectors as industry, medical, sports and education with the rapid development 3D printing technology and continual breakthrough of new material technology. Faced with the continual expansion of 3D printing market and the diversity and rapid growth of the scale of 3D printing devices, efficiently manage 3D print resources in the environment of distributed network manufacturing is a critical problem urgently to resolve. As a novel business paradigm, Cloud manufacturing can effectively integrate and manage manufacturing resources. Therefore, based on the cloud manufacturing paradigm, this study focuses on dynamic and static data based matching method for cloud 3D printing. In this paper, we propose a modeling framework to describe two models of the print task and print resource by model-based systems engineering. This modeling framework can support the efficient matching of the two types of models. Finally, the dynamic and static data based matching method can realistically simulate the supply-demand matching process of cloud 3D printing platform and provide a technical solution for quick supply-demand matching of large-scale resources in the environment of cloud manufacturing. During in the modeling process, we not only consider the static characteristics of 3D printers and analyze quantitatively all the parameter indicators of static characteristics, but also consider the dynamic characteristics of 3D printers to establish a universal dynamic data acquisition system, which can be used for real-time monitoring and automatic diagnosis of the health status of 3D printers. Therefore, this matching method has important theoretical significance and engineering value.