Abstract
Numerical control technology is the foundation of manufacturing modernization. However, due to the limitation of software and hardware resources, mainstream numerical control systems are increasingly unable to meet users’ increasing demands for intelligent and open functions. In terms of basic theoretical research, cloud computing is mainly based on universities and research institutes. It has made great contributions to the theoretical innovation of cloud computing. The cloud computing industry has a bright future, but compared with developed countries, the scale of China’s cloud computing market still needs to be improved, and it will still need to strive to catch up in the future. Based on the above background, the research content of this article is a heavy-duty CNC machine tool based on cloud computing. This paper first introduces a cloud computing scheduling algorithm based on dynamic substantial path driving and applies it to a CNC machine tool system. The combination of next-generation information technology such as big data is the starting point. Based on the analysis of the mainstream NC system structure at home and abroad, a cloud-based NC system architecture is proposed. Users can access the cloud application center through the CNC system to obtain services. The application center provides general software for the CNC industry. Users can also add their own customized software in the application center. Finally, through experimental simulation, the results prove that the Bayes reliability evaluation method based on the proposed heavy-duty CNC machine tool considering the degree of maintenance is more in line with reality. In addition, it can be seen from the Speedup average analysis that the performance of the proposed algorithm is surpass the algorithms HEFT, HEFT-Lookahead, and CEFT, which is 17.9% better than the algorithm HEFT, 14.5% better than the algorithm HEFT-Lookahead, and 10.2% better than the algorithm CEFT.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wang, Y., Li, J., Wang, H.H.: Cluster and cloud computing framework for scientific metrology in flow control. Clust. Comput. 22(1), 1–10 (2019)
Chen, J., Li, K., et al.: A parallel random forest algorithm for big data in a spark cloud computing environment. IEEE Trans. Parallel Distrib. Syst. 28(4), 919–933 (2017)
Cao, Z., Lin, J., Wan, C., et al.: Optimal cloud computing resource allocation for demand side management in smart grid. IEEE Trans. Smart Grid 8(4), 1943–1955 (2017)
Packer, R.G.: The ethics of cloud computing. Sci. Eng. Ethics 23(1), 21–39 (2017)
Shen, J., Zhou, T., Chen, X., Li, J., Susilo, W.: Anonymous and traceable group data sharing in cloud computing. IEEE Trans. Inf. Forensics Secur. 13(4), 912–925 (2018)
Cai, H., Xu, B., Jiang, L., et al.: IoT-based big data storage systems in cloud computing: Perspectives and challenges. IEEE Internet Things J. 4(1), 75–87 (2017)
Soofi, A.A., Khan, M.I.: A review on data security in cloud computing. Int. J. Comput. Appl. 96(2), 95–96 (2017)
Hashimoto, K., Masuyama, H., Kasahara, S., et al.: Performance analysis of backup-task scheduling with deadline time in cloud computing. J. Ind. Manag. Optim. (JIMO) 11(3), 867–886 (2017)
Chase, J., Niyato, D.: Joint optimization of resource provisioning in cloud computing. IEEE Trans. Serv. Comput. 10(3), 396–409 (2017)
Quirita, V.A.A., Happ, P.N., Feitosa, R.Q., et al.: A new cloud computing architecture for the classification of remote sensing data. IEEE J. Sel. Top. Appl. Earth Obs. & Remote. Sens. 10(2), 409–416 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Shao, J. (2021). Cloud Computing with Heavy CNC Machine Tools. In: Sugumaran, V., Xu, Z., Zhou, H. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. MMIA 2020. Advances in Intelligent Systems and Computing, vol 1233. Springer, Cham. https://doi.org/10.1007/978-3-030-51431-0_47
Download citation
DOI: https://doi.org/10.1007/978-3-030-51431-0_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51430-3
Online ISBN: 978-3-030-51431-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)