Abstract
Manufacturing data integration and sharing (MDIS) is an essential and key technology in big data-driven intelligent manufacturing mode. The preconditions of MDIS are generating product life cycle scenarios; strategy for acquiring data and using service according to generated scenarios to balance the interests of user, manufacturer, and environmental impacts; and standardization of data services. Firstly, this paper discusses integration process within enterprise from internal equipment-cell-shop-plant-enterprise then to external cloud. According to the different scenarios or phases, three kinds of MDIS methods are proposed, i.e., physical centralization by merging multiple data sources into an unique source for ensuring correctness of meta or general data, physical centralization by maintaining multiple data sources for promoting composed service of heterogeneous or various thematic data, and logic centralization by developing data directory for ensuring private data security and department or enterprise interests. Then, a hybrid manufacturing cloud architecture is proposed, and local critical data safely managed through private cloud, external required data, or its own provided services available through public cloud. Finally, taking machine tool and magnetic bearing resources as an example, a unified service modeling methods based on semantic ontology are used to facilitate the interconnection and interoperability between cyber space and physical space.
Similar content being viewed by others
References
Tao F, Cheng Y, Zhang L, Nee AYC (2015) Advanced manufacturing systems: socialization characteristics and trends. J Intell Manuf 18(3):1–16
Tao F, Zhao D, Hu Y, Zhou Z (2008) Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. IEEE Transactions on Industrial Informatics 4(4):315–327
Tao F, Hu Y, Zhou Z (2010) Correlation-aware resource service composition and optimal-selection in manufacturing grid. Eur J Oper Res 201(1):129–143
Tao F, Zhang L, Lu K, Zhao D (2011) Research on manufacturing grid resource service optimal-selection and composition framework. Enterprise Information Systems 6(2):237–264
Tao F, Zhao D, Zhang L (2010) Resource service optimal-selection based on intuitionistic fuzzy set and non-functionality QoS in manufacturing grid system. Knowl Inf Syst 25(1):185–208
Tao F, Laili Y, Xu L, Zhang L (2013) FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Transactions on Industrial Informatics 9(4):2023–2033
Tao F, Zuo Y, Xu LD, Zhang L (2014) IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Transactions on Industrial Informatics 10(2):1547–1557
Tao F, Cheng J, Cheng Y, Gu S, Zheng T, Yang H (2017) SDMSim: a manufacturing service supply–demand matching simulator under cloud environment. Robot Comput Integr Manuf 45:34–46
Ding X, Tian Y, Yu Y (2015) A real-time big data gathering algorithm based on indoor wireless sensor networks for risk analysis of industrial operations. IEEE Transactions on Industrial Informatics 12(3):1–1
Susto GA, Schirru A, Pampuri S, Mcloone S (2016) Supervised aggregative feature extraction for big data time series regression. IEEE Transactions on Industrial Informatics 12(3):1243–1252
Bao Q, Wang J, Cheng J. Research on ontology modeling of steel manufacturing process based on big data analysis// 2016:04005
Shtern M, Litoiu M (2014) A runtime sharing mechanism for Big Data platforms// International Conference on Network and Service Management. 304–307
Wu Y, Su M, Zheng W, Kai H, Zomaya AY (2015) Associative big data sharing in community clouds: the MeePo approach. IEEE Cloud Computing 2(6):64–73
Dong X, Li R, He H, Zhou W, Xue Z, Wu H (2015) Secure sensitive data sharing on a big data platform. Tsinghua Sci Technol 20(1):72–80
Banditwattanawong T, Masdisornchote M, Uthayopas P (2014) Economical and efficient big data sharing with i-Cloud// International Conference on Big Data and Smart Computing. 105–110
Dubey R, Gunasekaran A, Childe SJ, Wamba SF, Papadopoulos T (2016) The impact of big data on world-class sustainable manufacturing. Int J Adv Manuf Technol 84(1):631–645
Emani CK, Cullot N, Nicolle C (2015) Understandable big data: a survey. Computer Science Review 17:70–81
Wamba SF, Akter S, Edwards A, Chopin G (2015) How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study. Int J Prod Econ 165:234–246
Fan J, Han F, Liu H (2014) Challenges of big data analysis. National Science Review 1(2):293
Gu J, Zhang L (2014) Data, DIKW, Big Data and Data Science ☆. Procedia Computer Science 31(Complete):814–821
Siddiqa A, Hashem IAT, Yaqoob I, Marjani M, Gani A (2016) A survey of big data management: taxonomy and state-of-the-art. Journal of Network & Computer Applications 71:151–166
Castro S (2014) Optimizing your data management for big data. Journal of Direct, Data and Digital Marketing Practice 16(1):15–18
Xia Q, Xu Z, Liang W, Zomaya AY (2016) Collaboration- and fairness-aware big data management in distributed clouds. IEEE Transactions on Parallel & Distributed Systems 27(7):1941–1953
Kim JS, Whang KY, Kwon HY, Song IY (2016) PARADISE: big data analytics using the DBMS tightly integrated with the distributed file system. World Wide Web 19(3):299–322
Dutta D, Bose I (2015) Managing a Big Data project: the case of Ramco cements limited. Int J Prod Econ 165:293–306
Weeks R, Benade S (2015) The development of a generic servitization systems framework. Technol Soc 43(2):97–104
Vandermerwe S, Rada J (1988) Servitization of business: adding value by adding services. Eur Manag J 6(4):314–324
Opresnik D, Zanetti C, Taisch M. Servitization of the manufacturer’s value chain//Advances in production management systems. Sustainable Production and Service Supply Chains 2013:234–241.
Opresnik D, Taisch M (2015) The value of big data in servitization. Int J Prod Econ 165:174–184
Wang T, Ji P (2010) Understanding customer needs through quantitative analysis of Kano’s model. International Journal of Quality & Reliability Management 27(2):173–184
Li J, Tao F, Cheng Y, Zhao L (2015) Big Data in product lifecycle management. Int J Adv Manuf Technol 81(1):667–684
Tao F, Zuo Y, Xu LD, Lv L (2014) Internet of things and BOM-based life cycle assessment of energy-saving and emission-reduction of products. IEEE Transactions on Industrial Informatics 10(2):1252–1261
Tao F, Bi LN, Zuo Y, Nee AYC (2016) A hybrid group leader algorithm for green material selection with energy consideration in product design. CIRP Ann Manuf Technol 65(1):9–12
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Xiang, F., Yin, Q., Wang, Z. et al. Systematic method for big manufacturing data integration and sharing. Int J Adv Manuf Technol 94, 3345–3358 (2018). https://doi.org/10.1007/s00170-017-0575-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-017-0575-8