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Industrial Knowledge Management Tools Applied to Engineering Education Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Model-Based Engineering for the Integration of Manufacturing Systems with Advanced Analytics

Authors : David Lechevalier, Anantha Narayanan, Sudarsan Rachuri, Sebti Foufou, Y. Tina Lee

Published in: Product Lifecycle Management for Digital Transformation of Industries

Publisher: Springer International Publishing

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Abstract

To employ data analytics effectively and efficiently on manufacturing systems, engineers and data scientists need to collaborate closely to bring their domain knowledge together. In this paper, we introduce a domain-specific modeling approach to integrate a manufacturing system model with advanced analytics, in particular neural networks, to model predictions. Our approach combines a set of meta-models and transformation rules based on the domain knowledge of manufacturing engineers and data scientists. Our approach uses a model of a manufacturing process and its associated data as inputs, and generates a trained neural network model as an output to predict a quantity of interest. This paper presents the domain-specific knowledge that the approach should employ, the formal workflow of the approach, and a milling process use case to illustrate the proposed approach. We also discuss potential extensions of the approach.

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previous chapter An Onto-Based Interoperability Framework for the Connection of PLM and Production Capability Tools
next chapter Proposal of a Model-Driven Ontology for Product Development Process Interoperability and Information Sharing
Literature
1.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey (2011)
2.
Brown, B., Chui, M., Manyika, J.: Are you ready for the era of ‘big data’. McKinsey Q. 4(1), 24–35 (2011)
3.
Erzurumlu, T., Oktem, H.: Comparison of response surface model with neural network in determining the surface quality of moulded parts. Mater. Des. 28(2), 459–465 (2007)CrossRef
4.
Zhai, L.Y., Khoo, L.P., Fok, S.C.: Feature extraction using rough set theory and genetic algorithms—an application for the simplification of product quality evaluation. Comput. Ind. Eng. 43(4), 661–676 (2002)CrossRef
5.
Dabbas, R.M., Chen, H.N.: Mining semiconductor manufacturing data for productivity improvement—an integrated relational database approach. Comput. Ind. 45(1), 29–44 (2001)CrossRef
6.
Chien, C.F., Diaz, A.C., Lan, Y.B.: A data mining approach for analyzing semiconductor MES and FDC data to enhance overall usage effectiveness (OUE). Int. J. Comput. Intell. Syst. 7(sup2), 52–65 (2014)CrossRef
7.
Shin, S.J., Woo, J., Rachuri, S.: Predictive analytics model for power consumption in manufacturing. Procedia CIRP 15, 153–158 (2014)CrossRef
8.
Gupta, D., Gopalakrishnan, B.: Energy sensitive machining parameter optimisation. Int. J. Ind. Syst. Eng. 5(4), 405–423 (2010)
9.
Lechevalier, D., Narayanan, A., Rachuri, S.: Towards a domain-specific framework for predictive analytics in manufacturing. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 987–995. IEEE (2014)
10.
11.
12.
Wolbrecht, E., D’ambrosio, B., Paasch, R., Kirby, D.: Monitoring and diagnosis of a multistage manufacturing process using Bayesian networks. Ai Edam 14(01), 53–67 (2000)
13.
Correa, M., Bielza, C., de Ramirez, M.J., Alique, J.R.: A Bayesian network model for surface roughness prediction in the machining process. Int. J. Syst. Sci. 39(12), 1181–1192 (2008)CrossRefMATH
14.
Abouelatta, O.B., Madl, J.: Surface roughness prediction based on cutting parameters and tool vibrations in turning operations. J. Mater. Process. Technol. 118(1), 269–277 (2001)CrossRef
15.
Tsai, Y.H., Chen, J.C., Lou, S.J.: An in-process surface recognition system based on neural networks in end milling cutting operations. Int. J. Mach. Tools Manuf. 39(4), 583–605 (1999)CrossRef
16.
Kurz, D., Kaspar, J., Pilz, J.: Dynamic maintenance in semiconductor manufacturing using Bayesian networks. In: 2011 IEEE Conference on Automation Science and Engineering (CASE), pp. 238–243. IEEE (2011)
17.
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall (2004)
18.
Heaton, J.: Introduction to Neural Networks with Java. Heaton Research Inc., St. Louis (2008)
19.
Lechevalier, D., Hudak, S., Ak, R., Lee, Y.T., Foufou, S.: A neural network meta-model and its application for manufacturing. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 1428–1435. IEEE (2015)
20.
21.
Sheela, K.G., Deepa, S.N.: Review on methods to fix number of hidden neurons in neural networks. Mathematical Problems in Engineering (2013)
22.
Park, J., et al.: A generalized data-driven energy prediction model with uncertainty for a milling machine tool using Gaussian Process. In: ASME 2015 International Manufacturing Science and Engineering Conference (2015)
23.
Nannapaneni, S., Mahadevan, S.: Uncertainty quantification in performance evaluation of manufacturing processes. In: 2014 IEEE International Conference on Big Data (Big Data). IEEE (2014)
Metadata
Title
Model-Based Engineering for the Integration of Manufacturing Systems with Advanced Analytics
Authors
David Lechevalier
Anantha Narayanan
Sudarsan Rachuri
Sebti Foufou
Y. Tina Lee
Copyright Year
2016
Book
Product Lifecycle Management for Digital Transformation of Industries

Print ISBN: 978-3-319-54659-9

Electronic ISBN: 978-3-319-54660-5

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-54660-5_14

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