A survey of the advancing use and development of machine learning in smart manufacturing

Journal of Manufacturing Systems - Tập 48 - Trang 170-179 - 2018
Michael Sharp1, Ronay Ak1, Thomas Krämer1
1Engineering Laboratory, National Institute of Standards and Technology, 100 Bureau Drive, Stop 8260, Gaithersburg, MD 20899, USA

Tóm tắt

Từ khóa


Tài liệu tham khảo

Piddington, 1993, An IMS test case – global manufacturing, 11

Thoben, 2017, “industrie 4.0” and smart manufacturing – a review of research issues and application examples, Int. J. Autom. Technol., 11, 4, 10.20965/ijat.2017.p0004

Pugh, 1991

Yang, 2009

da Silva, 2014, Integration of computer simulation in design for manufacturing and assembly, Int. J. Prod. Res., 52, 2851, 10.1080/00207543.2013.853887

Garbie, 2013, DFSME: design for sustainable manufacturing enterprises (an economic viewpoint), Int. J. Prod. Res., 51, 479, 10.1080/00207543.2011.652746

Bolten, 2008

Hedberg, 2017, Identified research directions for using manufacturing knowledge earlier in the product life cycle, Int. J. Prod. Res., 55, 819, 10.1080/00207543.2016.1213453

Helu, 2017, Reference architecture to integrate heterogeneous manufacturing systems for the digital thread, CIRP J. Manuf. Sci. Technol., 10.1016/j.cirpj.2017.04.002

Energetics Inc, 2015

Gao, 2015, Cloud-enabled prognosis for manufacturing, CIRP Ann. – Manuf. Technol., 64, 749, 10.1016/j.cirp.2015.05.011

Helu, 2015, 86

Li, 2006, Real-time collaborative design with heterogeneous CAD systems based on neutral modeling commands, J. Comput. Inf. Sci. Eng., 7, 113, 10.1115/1.2720880

Hedberg, 2017, Towards a lifecycle information framework and technology in manufacturing, J. Comput. Inf. Sci. Eng., 17, 10.1115/1.4034132

Jennings, 2016, Forecasting obsolescence risk and product life cycle with machine learning, IEEE Trans. Compon. Packag. Manuf. Technol., 6, 1428, 10.1109/TCPMT.2016.2589206

Li, 2018, An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction, Reliab. Eng. Syst. Saf., 10.1201/9781315273860

Wang, 2018, Deep learning for smart manufacturing: methods and applications, J. Manuf. Syst., 48, 144, 10.1016/j.jmsy.2018.01.003

The International Society of Automation, 2010

Johnsson, 2006

Chonde, 2015, Content mining to support design, 1672

Chonde, 2016

Dumais, 2004, Latent semantic analysis, Annu. Rev. Inf. Sci. Technol., 38, 188, 10.1002/aris.1440380105

Leskovec, 2014

Deng, 2011, Using least squares support vector machines for the airframe structures manufacturing cost estimation, Int. J. Prod. Econ., 131, 701, 10.1016/j.ijpe.2011.02.019

Yeh, 2012, Application of machine learning methods to cost estimation of product life cycle, Int. J. Comput. Integr. Manuf., 25, 340, 10.1080/0951192X.2011.645381

Garcia, 2014

Woodward, 2010

Yusof, 2011, Harmony search algorithm for flexible manufacturing system (FMS) machine loading problem, 3rd conference on data mining and optimization (DMO), 26, 10.1109/DMO.2011.5976500

Geem, 2001, A new heuristic optimization algorithm: harmony search, Simulation, 76, 60, 10.1177/003754970107600201

Wu, 2007, A neural network integrated decision support system for condition-based optimal predictive maintenance policy, IEEE Trans. Syst. Man Cybern. A: Syst. Hum., 37, 226, 10.1109/TSMCA.2006.886368

Choo, 2016, Adaptive multi-scale prognostics and health management for smart manufacturing systems, Int. J. Progn. Health Manage. (IJPHM) – Special Issue: PHM Smart Manuf. Syst., 7, 014

Heddy, 2015, Linear temporal logic (LTL) based monitoring of smart manufacturing systems, 10

Kumar, 2016, A big data mapreduce framework for fault diagnosis in cloud-based manufacturing, Int. J. Prod. Res., 54, 7060, 10.1080/00207543.2016.1153166

Xu, 2012, From cloud computing to cloud manufacturing, Robot. Comput.-Integr. Manuf., 28, 75, 10.1016/j.rcim.2011.07.002

MTConnect Institute, 2014

International Standards Organization, 2014

International Standards Organization, 2007

Brodsky, 2016, A system and architecture for reusable abstractions of manufacturing processes, 2016 IEEE international conference on big data (big data), 2004, 10.1109/BigData.2016.7840823

Brodsky, 2016, Analysis and optimization based on reusable knowledge base of process performance models, Int. J. Adv. Manuf. Technol., 1

2017

P. Hughes, A. Nwaigwe, S. Packham, A. Dunstone Gray, About gitlab, 2017-02-09. http://www.webcitation.org/6sxUV6u26 (accessed 24.08.17).

Bloomfield, 2012, Interoperability of manufacturing applications using the core manufacturing simulation data (CMSD) standard information model, Comput. Ind. Eng., 62, 1065, 10.1016/j.cie.2011.12.034

Leong, 2006, A core manufacturing simulation data information model for manufacturing applications

Dimensional Metrology Standards Consortium, Part 1: Overview and fundamental principles in quality information framework (QIF) – an integrated model for manufacturing quality information, Standard, 2014.

Aruväli, 2014, Digital object memory based monitoring solutions in manufacturing processes, Proc. Eng., 69, 449, 10.1016/j.proeng.2014.03.011

Gröger, 2013, Leveraging apps in manufacturing: a framework for app technology in the enterprise, Proc. CIRP, 7, 664, 10.1016/j.procir.2013.06.050

McIlroy, 2016, Fresh apps: an empirical study of frequently-updated mobile apps in the google play store, Empir. Softw. Eng., 21, 1346, 10.1007/s10664-015-9388-2

Dekkers, 2013, The interface between “product design and engineering” and manufacturing: a review of the literature and empirical evidence, Int. J. Prod. Econ., 144, 316, 10.1016/j.ijpe.2013.02.020

Barthelmey, 2014, Cyber physical systems for life cycle continuous technical documentation of manufacturing facilities, Procedia CIRP, 17, 207, 10.1016/j.procir.2014.01.050