Data-driven smart manufacturing

Journal of Manufacturing Systems - Tập 48 - Trang 157-169 - 2018
Fei Tao1, Qinglin Qi1, Ang Liu2, Andrew Kusiak3
1School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
2School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, 2053, Australia
3Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, USA

Tài liệu tham khảo

Tao, 2017, New IT driven service-oriented smart manufacturing: framework and characteristics, IEEE Trans Syst Man Cybern Syst, 10.1109/TSMC.2017.2723764 O’Donovan, 2015, An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities, J Big Data, 2, 1 Yin, 2015, Big data for modern industry: challenges and trends [point of view], Proc IEEE, 103, 143, 10.1109/JPROC.2015.2388958 Shao, 2014, Data analytics using simulation for smart manufacturing, Proceedings of the 2014 Winter Simulation Conference, 2192, 10.1109/WSC.2014.7020063 Kusiak, 2017, Smart manufacturing must embrace big data, Nature, 544, 23, 10.1038/544023a Mourtzis, 2016, Industrial big data as a result of IoT adoption in manufacturing, Procedia CIRP, 55, 290, 10.1016/j.procir.2016.07.038 Tao, 2014, IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing, IEEE Trans Ind Inf, 10, 1547, 10.1109/TII.2014.2306397 Hashem, 2015, The rise of big data on cloud computing: review and open research issues, Inf Syst, 47, 98, 10.1016/j.is.2014.07.006 Tao, 2014, CCIoT-CMfg: cloud computing and internet of things-based cloud manufacturing service system, IEEE Trans Ind Inf, 10, 1435, 10.1109/TII.2014.2306383 Lee, 2003, Visualizations of binary data: a comparative evaluation, Int J Hum Comput Stud, 59, 569, 10.1016/S1071-5819(03)00082-X Wuest, 2016, Machine learning in manufacturing: advantages, challenges, and applications, Prod Manuf Res, 4, 23 Obitko, 2013, Big data challenges in industrial automation, Proceedings of International Conference on Industrial Applications of Holonic and Multi-Agent Systems, 305, 10.1007/978-3-642-40090-2_27 Galletti, 2013, 1 Dubey, 2016, The impact of big data on world-class sustainable manufacturing, Int J Adv Manuf Technol, 84, 631, 10.1007/s00170-015-7674-1 Zhang, 2017, A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products, J Clean Prod, 142, 626, 10.1016/j.jclepro.2016.07.123 Bahga, 2012, Analyzing massive machine maintenance data in a computing cloud, IEEE Trans Parallel Distrib Syst, 23, 1831, 10.1109/TPDS.2011.306 Lee, 2017, Framework and development of fault detection classification using IoT device and cloud environment, J Manuf Syst, 43, 257, 10.1016/j.jmsy.2017.02.007 Munirathinam, 2014, Big data predictive analytics for proactive semiconductor equipment maintenance, Proceedings of 2014 IEEE International Conference on Big Data, 893, 10.1109/BigData.2014.7004320 Chan, 2018, Data-driven cost estimation for additive manufacturing in cyber manufacturing, J Manuf Syst, 46, 115, 10.1016/j.jmsy.2017.12.001 Schleich, 2017, Shaping the digital twin for design and production engineering, CIRP Ann Manuf Technol, 66, 141, 10.1016/j.cirp.2017.04.040 Hu, 2013, Evolving paradigms of manufacturing: from mass production to mass customization and personalization, Procedia CIRP, 7, 3, 10.1016/j.procir.2013.05.002 Balakrishnan, 1999, Manufacturing in the digital age: exploiting information technologies for product realization, Inf Syst Front, 1, 25, 10.1023/A:1010012712144 Bughin, 2010, Clouds, big data, and smart assets: ten tech-enabled business trends to watch, McKinsey Q, 56, 75 Li, 2015, Big data in product lifecycle management, Int J Adv Manuf Technol, 81, 667, 10.1007/s00170-015-7151-x Chen, 2014, Big data: a survey, Mobile Networks Appl, 19, 171, 10.1007/s11036-013-0489-0 Lee, 2013, Recent advances and trends in predictive manufacturing systems in big data environment, Manuf Lett, 1, 38, 10.1016/j.mfglet.2013.09.005 Siddiqa, 2016, A survey of big data management: taxonomy and state-of-the-art, J Network Comput Appl, 71, 151, 10.1016/j.jnca.2016.04.008 Zhang, 2015, Real-time information capturing and integration framework of the internet of manufacturing things, Int J Comput Integr Manuf, 28, 811, 10.1080/0951192X.2014.900874 Tao, 2017, IIHub: an industrial internet-of-Things hub towards smart manufacturing based on cyber-Physical system, IEEE Trans Ind Inf Guerriero, 2010, A dynamic URL assignment method for parallel web crawler, Proceedings of 2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 119, 10.1109/CIMSA.2010.5611764 Gandomi, 2015, Beyond the hype: big data concepts, methods, and analytics, Int J Inf Manage, 35, 137, 10.1016/j.ijinfomgt.2014.10.007 Nicolae, 2011, BlobSeer: next-generation data management for large scale infrastructures, J Parallel Distrib Comput, 71, 169, 10.1016/j.jpdc.2010.08.004 Agrawal, 2010, Data management challenges in cloud computing infrastructures, Proceedings of 6th international workshop on databases in networked information systems, 1 Huang, 2015, An empirical analysis of data preprocessing for machine learning-based software cost estimation, Inf Software Technol, 67, 108, 10.1016/j.infsof.2015.07.004 Begoli, 2012, Design principles for effective knowledge discovery from big data, Proceedings of 2012 Software Architecture and European Conference on Software Architecture, 215, 10.1109/WICSA-ECSA.212.32 Mittal, 2017, Smart manufacturing: characteristics, technologies and enabling factors Proceedings of the Institution of Mechanical Engineers, Part B: J Eng Manuf Tao, 2017, Digital twin-driven product design, manufacturing and service with big data, Int J Adv Manuf Technol Liu, 2013, Identifying helpful online reviews: a product designer’s perspective, Comput Aided Des, 45, 180, 10.1016/j.cad.2012.07.008 Bennane, 2012, LAD-CBM; new data processing tool for diagnosis and prognosis in condition-based maintenance, J Intell Manuf, 23, 265, 10.1007/s10845-009-0349-8 Kusiak, 2012, Analyzing bearing faults in wind turbines: a data-mining approach, Renew Energ, 48, 110, 10.1016/j.renene.2012.04.020 Kusiak, 2007, Computational intelligence in product design engineering: review and trends, IEEE Trans Syst Man Cybern Part C (Appli Rev), 37, 766, 10.1109/TSMCC.2007.900669 Qi, 2016, Mining customer requirements from online reviews: a product improvement perspective, Inf Manage, 53, 951, 10.1016/j.im.2016.06.002 Innovation, 2009, A data-driven approach, Int J Prod Econ, 122, 440, 10.1016/j.ijpe.2009.06.025 Da Cunha, 2010, Selection of modules for mass customisation, Int J Prod Res, 48, 1439, 10.1080/00207540802473989 Wu, 2017, Democratizing digital design and manufacturing using high performance cloud computing: performance evaluation and benchmarking, J Manuf Syst, 43, 316, 10.1016/j.jmsy.2016.09.005 Ji, 2017, Big data analytics based fault prediction for shop floor scheduling, J Manuf Syst, 43, 187, 10.1016/j.jmsy.2017.03.008 Cheng, 2015, Dynamic supply-demand matching for manufacturing resource services in service-oriented manufacturing systems: a hypernetwork-based solution framework, Proceedings of ASME International Manufacturing Science and Engineering Conference (MSEC2015), 2015, 8 Song, 2010, Multiobjective optimization of temporal processes, IEEE Trans Syst Man Cybern Part B (Cybern), 40, 845, 10.1109/TSMCB.2009.2030667 Tao, 2008, Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system, IEEE Trans Ind Inf, 4, 315, 10.1109/TII.2008.2009533 Zhong, 2016, Visualization of RFID-enabled shopfloor logistics big data in cloud manufacturing, Int J Adv Manuf Technol, 84, 5, 10.1007/s00170-015-7702-1 Zhong, 2015, A big data approach for logistics trajectory discovery from RFID-enabled production data, Int J Prod Econ, 165, 260, 10.1016/j.ijpe.2015.02.014 Lu, 2017, A RFID-enabled positioning system in automated guided vehicle for smart factories, J Manuf Syst, 44, 179, 10.1016/j.jmsy.2017.03.009 Qin, 2012, Survey on data-driven industrial process monitoring and diagnosis, Annu Rev Control, 36, 220, 10.1016/j.arcontrol.2012.09.004 Qin, 2014, Process data analytics in the era of big data, AIChE J, 60, 3092, 10.1002/aic.14523 Abonyi, 2003, Data-driven generation of compact: accurate, and linguistically sound fuzzy classifiers based on a decision-tree initialization, Int J Approximate Reasoning, 32, 1, 10.1016/S0888-613X(02)00076-2 Hinton, 2006, Reducing the dimensionality of data with neural networks, Science, 313, 504, 10.1126/science.1127647 Kim, 2017, Imbalanced classification of manufacturing quality conditions using cost-sensitive decision tree ensembles, Int J Comput Integr Manuf Köksal, 2011, A review of data mining applications for quality improvement in manufacturing industry, Expert Syst Appl, 38, 13448, 10.1016/j.eswa.2011.04.063 Sata, 2017, Bayesian inference-based investment-casting defect analysis system for industrial application, Int J Adv Manuf Technol, 90, 3301, 10.1007/s00170-016-9614-0 He, 2017, Big data oriented root cause identification approach based on Axiomatic domain mapping and weighted association rule mining for product infant failure, Comput Ind Eng, 109, 253, 10.1016/j.cie.2017.05.012 Verma, 2013, Modeling and prediction of gearbox faults with data-mining algorithms, J Solar Energy Eng, 135, 10.1115/1.4023516 Si, 2011, Remaining useful life estimation–A review on the statistical data driven approaches, Eur J Oper Res, 213, 1, 10.1016/j.ejor.2010.11.018 Zhang, 2015, Data-driven minimization of pump operating and maintenance cost, Eng Appl Artif Intell, 40, 37, 10.1016/j.engappai.2015.01.003 Mourtzis, 2016, Energy consumption estimation for machining processes based on real-time shop floor monitoring via wireless sensor networks, Procedia CIRP, 57, 637, 10.1016/j.procir.2016.11.110 Zuo, 2017, An Internet of things and cloud-based approach for energy consumption evaluation and analysis for a product, Int J Comput Integr Manuf Pizoń, 2016, Perspectives for fog computing in manufacturing, Appl Comput Sci, 12, 37 Wu, 2017, A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing, J Manuf Syst, 43, 25, 10.1016/j.jmsy.2017.02.011