Deep Learning for Smart Manufacturing: Methods and Applications
Keywords:
smart manufacturing, Deep learning technologies, proliferation of sensors, Internet of things,Abstract
Smart manufacturing leverages advanced data analytics alongside physical science to enhance system performance and decision-making processes. With the proliferation of sensors and the Internet of Things (IoT), there is a growing necessity to manage vast amounts of manufacturing data characterized by high volume, velocity, and variety. Deep learning techniques offer sophisticated analytics tools for processing and analyzing such big manufacturing data. This paper presents a comprehensive survey of commonly utilized deep learning algorithms and discusses their applications in making manufacturing processes "smart." It begins by discussing the evolution of deep learning technologies and their advantages over traditional machine learning approaches. The paper then delves into computational methods based on deep learning specifically designed to enhance system performance in manufacturing. Various representative deep learning models are comparatively discussed. Finally, the paper highlights emerging research topics in deep learning and summarizes future trends and challenges associated with utilizing deep learning for smart manufacturing.
Downloads
References
Putnik G, Sluga A, ElMaraghy H, Teti R, Koren Y, Tolio T, et al. Scalability inmanufacturing systems design and operation: state-of-the-art and future developments roadmap. CIRP Ann Manuf Technol 2013;62(2):751–74.
Lee YT, Kumaraguru S, Jain S, Hatim Q, Rob- inson S, Helu M, et al. A classification scheme for smart manufacturing systems’ performance metrics. Smart Sustain Manuf Syst 2017;1(1):52–74.
Hu T, Li P, Zhang C, Liu R. Design and application of a real-time industrial Ethernet protocol under Linux using RTAI. Int J Comput Integr Manuf 2013;26(5):429–39.
Ye Y, Hu T, Zhang C, Luo W. Design and devel- opment of a CNC machining process knowledge base using cloud technology. Int J Adv Manuf Technol 2016:1–13.
Tao F, Qi Q. New IT driven service- oriented smart manufacturing: framework and characteristics. IEEE Trans Syst Man Cybern Syst 2017;99:1– 11.
Ang J, Goh C, Saldivar A, Li Y. Energy-efficient through-life smart design, manufacturing and op- eration of ships in an industry 4.0 environment. Energies 2017;10(5):610.
Huang Z, Hu T, Peng C, Hou M, Zhang C. Re- search and development of industrial real-time Ethernet performance testing system used for CNC system. Int J Adv Manuf Technol 2016;83(5–8):1199–207.
Lalanda P, Morand D, Chollet S. Autonomic media- tion middleware for smart manufacturing. IEEE In- ternet Comput 2017;21(1):32–9.
Smart Manufacturing Coalition. Manufacturing growth continues despite uncertain economy, ac- cording to ASQ outlook survey; 2013. https:// smartmanufacturingcoalition.org/sites/de- fault/files/12.16.13 manufacturing outlook sur- vey.pdf. [Accessed 10 Sepember 2017].
Wang L, Törngren M, Onori M. Current status and ad-vancement of cyber-physical systems inmanufacturing. J Manuf Syst2015;37:517–27.
Wang P, Gao RX, Fan Z. Cloud computing for cloud manufacturing: benefits and limitations. J Manuf Sci Eng 2015;137:1–10.
Lu Y, Xu X, Xu J. Development of a hybrid manufac- turing cloud. J Manuf Syst 2014;33(4):551–66.
Wu D, Rosen DW, Schaefer D. Cloud-based de- sign and manufacturing: status and promise. Comput Aided Des 2015;59:1–14.
Choudhary AK, Harding JA, Tiwari MK. Data min- ing in manufacturing: a review based on the kind of knowledge. J Intell Manuf 2009;20(5):501–21.
Lade P, Ghosh R, Srinivasan S. Manufacturing analytics and industrial internet of things. IEEE Intell Syst 2017;32(3):74–9.
Monostori L, Márkus A, Brussel HV, Westkämpfer E. Machine learning approaches to manufacturing. CIRP Ann Manuf Technol 1996;45(2):675–712.
Teti R, Jemielniak K, O’Donnell G, Dornfeld D. Advanced monitoring ofmachining operations. CIRP Ann Manuf Technol 2010;59(2):717–39.
Helu M, Libes D, Lubell J, Lyons K, Morris K. Ena- bling smart manufacturing technologies for deci- sion-making support. Proceedings of the ASME in- ternational design engineering technical confer- ences and computers and information in engineer- ing conference (IDETC/CIE) 2016:1–10.
Wuest T, Weimer D, Irgens C, Klaus DT. Machine learning in manufacturing: advantages, challenges, and applications. Prod Manuf Res 2016;4(1):23–45.
Gao R, Wang L, Teti R, Dornfeld D, Kumara S, Helu M, et al. Cloud-enabled prognosis for manu- facturing. CIRP Ann Manuf Technol 2015;64(2):749–72.
Wu D, Jennings C, Terpenny J, Gao RX, Kumara S. A comparative study on machine learning algo- rithms for smart manufacturing: tool wear prediction using random forests. J Manuf Sci Eng 2017;139(7):1–10.
Gartner’s Top10 strategic technology trendsfor; 2017. http://www.gartner. com/smarterwithgart- ner/gartners-top-10- technology-trends-2017/. [Ac- cessed 13 August 2017].
Mcculloch WS, Pitts WH. A logical calculus of the ideas immanent innervous activity. Bull Math Biophys 1943;5(4):115–33.
Samuel AL. Some studies in machine learning us- ing the game of checkers II— recent progress. Annu Rev Autom Program 2010;44(1–2):206–26.
Rosenblatt F. Perceptron simulation 1960;48(3):301–9.
Widrow B, Hoff ME. Adaptive switching cir- cuits. Cambridge: MIT Press; 1960.
Minsky M, Perceptrons Papert S. Am J Psychol 1988;84(3):449– 52.
Tank DW, Hopfield JJ. Neural computation by con- centrating information in time. Proc Natl Acad Sci USA 1987;84(7):1896.
Werbos PJ. Backpropagation through time: what it does and how to do it. Proc IEEE 1990;78(10):1550–60.
Sussmann HJ. Learning algorithms for Boltz- mann machines. 27th IEEE conference on deci- sion and control 1988;1:786–91.
Vapnik VN. An overview of statistical learning theory. IEEE Trans Neural Netw 1998;10(5):988–99.
Smolensky P. Information processing in dynamical systems: foundations of harmony theory. Parallel distributed processing: explorations in the micro- structure of cognition. Cambridge: MIT Press; 1986.
Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back- propagat- ing errors. Nature 1986;323(6088):533–6.
Hihi SE, Hc-J MQ, Bengio Y. Hierarchical re- current neural networks for Long-Term depend- encies. Adv Neural Inf Process Syst 1995;8:493–9.
Hochreiter S, Schmidhuber J. Long short- Term memory. Neural Comput 1997;9(8):1735.
Lécun Y, Bottou L, Bengio Y, Haffner P. Gradient- based learning applied to document recognition. Proc IEEE 1998;86(11):2278–324.
Hinton GE, Salakhutdinov RR. Reducing the di- mensionality of data with neural network. Sci- ence 2006;313(5786):504–7.
Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput 2014;18(7):1527– 54.
Deng L, Seltzer M, Yu D, Acero A, Mohamed A Hinton GE. Binary coding of speech spectrograms using a deep auto-encoder. Proceedings of 11th an- nual conference of the international speech commu- nication association 2010;3:1692–5.
Schölkopf B, Platt J, Hofmann T. Efficient learning of sparse representations with an energy-Based model. Proceedings of advances in neural infor- mation processingsystems 2006:1137–44.
Ranzato MA, Boureau YL, Lecun Y. Sparse fea- ture learning for deep belief networks. Proceed- ings of international conference on neural infor- mation processing systems 2007;20:1185–92.
Salakhutdinov RR, Hinton GE. Deep Boltzmann ma- chines. J Mach Learn Res 2009;5(2):1967–2006.
Larochelle H, Lajoie I, Bengio Y, Manzagol PA. Stacked denoising autoencoders: learning useful representations in a deep network with a local de- noising criterion. J Mach Learn Res 2010;11(12):3371–408.
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolution neural networks. International conference on neural information processing systems 2012;25:1097–105.
Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversar- ial nets. Int Conf Neural Inf Process Syst 2014;3:2672–80.
Wang Y, Huang M, Zhao L, Zhu X. Attention-based LSTM for aspect-level sentiment classification. Pro- ceedings of conference on empirical methods in nat- ural language processing 2016:606–15.
Poggio T, Smale S. The mathematics of learning: dealing with data. Not Am Math Soc 2003;50(5):537–44.
Kusiak A. Smart manufacturing must em- brace big data. Nature 2017;544(7648):23–5.
Ince T, Kiranyaz S, Eren L, Askar M, GabboujM. Real-time motor fault detection by 1-D con- volution neural networks. IEEE Trans Ind Elec- tron 2016;63(11):7067–75.Hassanzadeh A, Kaarna A, Kauranne T. Unsu- pervised multi- manifold classification of hyper- spectral remote sensing images with contractive Autoencoder. Neurocomputing 2017;257:67– 78.Caffe2. https://caffe2.ai/. 2017 [Accessed 20 October 2017].
Theano. http://deeplearning.net/software/theano/index.html#. 2017 [Accessed 20 October 2017].
Google TensorFlow. https://www.ten- sorflow.org/. 2017 [Accessed 20 October 2017]. Pytorch. http://pytorch.org/. 2017 [Accessed 20 October 2017].
Microsoft Cognitive Toolkit. https://www.mi-crosoft.com/en- us/cognitive- toolkit. 2017 [Ac- cessed 20 October 2017]. Google Google cloud machine learning. https://cloud.google.com/products/ machine-learn- ing/. 2017 [Accessed 20 October 2017].
Amazon Web Service. Amazo AI, https://aws.amazon.com/amazon-ai/. 2017 [Ac- cessed 20 October 2017.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
