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DATA FOR ENGINEERING DESIGN: MAPS AND GAPS

Published online by Cambridge University Press:  27 July 2021

Filippo Chiarello ,
Elena Coli ,
Vito Giordano ,
Gualtiero Fantoni  and
Andrea Bonaccorsi
Filippo Chiarello
Affiliation:
Department of Energy, Systems, Territory and Construction Engineering, University of Pisa B4DS Lab - Business Engin
Elena Coli*
Affiliation:
Department of Information Engineering, University of Pisa B4DS Lab - Business Engin
Vito Giordano
Affiliation:
Department of Information Engineering, University of Pisa B4DS Lab - Business Engin
Gualtiero Fantoni
Affiliation:
Department of Civil and Industrial Engineering, University of Pisa B4DS Lab - Business Engin
Andrea Bonaccorsi
Affiliation:
Department of Energy, Systems, Territory and Construction Engineering, University of Pisa B4DS Lab - Business Engin
*
Coli, Elena, University of Pisa, Department of Information Engineering, Italy, [email protected]

Abstract

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Data, information and knowledge are strongly involved in Engineering Design (ED) process. Despite the crucial role played by data in the design process, there is a lack of studies about how different data are used and generated by the various phases of the ED process. This study is a first attempt to fill this gap by mapping which data types are involved in the different ED phases from a research perspective.

In order to achieve this objective, we used a methodology based on Text Mining. Firstly, we retrieve a corpus of scientific papers related to ED; then, we build two lexicons to recognize ED phases and data types; finally, we collect these entities within ED papers and map the relations between them.

The methodology application allows the building of a network graph for visualizing the relations among data lexicon and ED lexicon. Then, we investigate the specific relations among data types and ED phases by building a heatmap to investigate data types from 3 different perspective.

The insight coming from our analysis shows that ED studies have a great potential in the usage of many data sources, but also that there exist some gaps to be solved in order to reach a more effective data usage in the context of ED.

Keywords

Semantic data processingKnowledge managementInformation managementData TypesText Mining
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 821 - 830
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

References

Ackoff, R.L. (1989), “From Data to Wisdom”, Journal of Applied Systems Analysis, Vol. 16, No. 1, pp. 3-9.Google Scholar
Bang, H. and Selva, D. (2016), “iFEED: Interactive feature extraction for engineering design”, Proceedings of the ASME 2016 International Design Engineering Technical Conference, Charlotte, August 21–24, 2016. https://doi.org/10.1115/DETC2016-60077CrossRefGoogle Scholar
Bartholomew, S. R., Reeve, E. M., Veon, R., Goodridge, W., Lee, V. R. and Nadelson, L. (2017), “Relationships between access to mobile devices, student self-directed learning, and achievement”, Journal of Technology Education, Vol. 29(1), pp.2-24. https://doi.org/10.21061/jte.v29i1.a.1CrossRefGoogle Scholar
Bi, Z. (2019), Finite Element Analysis Applications, Academic Press.Google Scholar
Chaudhari, A. M., Bilionis, I., and Panchal, J. H. (2020), “Descriptive models of sequential decisions in engineering design: An experimental study”, Journal of Mechanical Design, Vol. 142(8): 081704. https://doi.org/10.1115/1.4045605CrossRefGoogle Scholar
Chiarello, F., Cimino, A., Fantoni, G. and Dell'Orletta, F. (2018), “Automatic Users Extraction from Patents”, World Patent Information, Vol. 54, pp. 28–38. https://doi.org/10.1016/j.wpi.2018.07.006CrossRefGoogle Scholar
Chiarello, F., Melluso, N., Bonaccorsi, A. and Fantoni, G. (2019), “A text mining based map of engineering design: Topics and their trajectories over time”, Proceedings of the design society: International conference on engineering design, Vol. 1, No. 1, pp. 2765-2774. Cambridge University Press. https://doi.org/10.1017/dsi.2019.283Google Scholar
Chiarello, F., Bonaccorsi, A. and Fantoni, G. (2020), “Technical Sentiment Analysis. Measuring Advantages and Drawbacks of New Products Using Social Media”, Computers in Industry, Vol. 123: 103299. https://doi.org/10.1016/j.compind.2020.103299CrossRefGoogle Scholar
Chiarello, F., Belingheri, P., Fantoni, G. (2021), “Data Science for Engineering Design: State of the Art and Future Directions”, Computers in Industry (in press) 10.1016/j.compind.2021.103447CrossRefGoogle Scholar
Chu, K.C. and Culbert, D.J. (1997), “Efficient high speed trie search process”, U.S. Patent No. 5,640,551. Washington, DC: U.S. Patent and Trademark Office. Available at: https://patents.google.com/patent/US5640551Google Scholar
Deutsch, R. (2015), “Leveraging data Across the Building Lifecycle”, Procedia Engineering, Vol. 118, pp. 260-267. https://doi.org/10.1016/j.proeng.2015.08.425CrossRefGoogle Scholar
Dieter, G. E. and Schmidt, L. C. (2009), Engineering design, Boston: McGraw-Hill Higher Education.Google Scholar
Fantoni, G., Coli, E., Chiarello, F., Apreda, R., Dell'Orletta, F. and Pratelli, G. (2021), “Text mining tool for translating terms of contract into technical specifications: Development and application in the railway sector”, Computers in Industry, Vol. 124C: 103357. https://doi.org/10.1016/j.compind.2020.103357CrossRefGoogle Scholar
Haik, Y., Sivaloganathan, S. and Shahin, T. M. (2018), Engineering design process, Nelson Education.Google Scholar
Kim, S., Bracewell, R. and Wallace, K. (2005), “A framework for design rationale retrieval”, in Proc. of ICED 05, 15th International Conference on Engineering Design, Melbourne, Australia, 252-253.Google Scholar
Krupa, G.P. (2019), “Application of Agile Model-Based Systems Engineering in aircraft conceptual design”, The Aeronautical Journal, Vol. 123(1268), pp.1561–1601. https://doi.org/10.1017/AER.2019.53CrossRefGoogle Scholar
Lin, J., Rohleder, C. and Nurcan, S. (2018), “Material Management in LCA Integrated PLM” in Proc. of 2018 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2018, Stuttgart, Germany, 17-20 June 2018. https://doi.org/10.1109/ICE.2018.8436374CrossRefGoogle Scholar
Liu, Q., Wang, K., Li, Y. and Liu, Y. (2020), “Data-Driven Concept Network for Inspiring Designers’ Idea Generation”, Journal of Computing and Information Science in Engineering, 20(3): 031004. https://doi.org/10.1115/1.4046207CrossRefGoogle Scholar
Melluso, N., Bonaccorsi, A., Chiarello, F. and Fantoni, G. (2020), “Rapid detection of fast innovation under the pressure of COVID-19”, PloS one, 15(12): e0244175. https://doi.org/10.1371/journal.pone.0244175CrossRefGoogle ScholarPubMed
Moehrle, M.G. and Caferoglu, H. (2019), “Technological speciation as a source for emerging technologies. Using semantic patent analysis for the case of camera technology”, Technological Forecasting and Social Change, Vol. 146, pp.776-784. https://doi.org/10.1016/j.techfore.2018.07.049CrossRefGoogle Scholar
Nadeau, D. and Sekine, S. (2007), “A survey of named entity recognition and classification”, Lingvisticae Investigationes, Vol. 30, no. 1, pp. 3–26. https://doi.org/10.1075/li.30.1.03CrossRefGoogle Scholar
Pahl, G., Beitz, W., Feldhusen, J. and Grote, K.H. (2007), Engineering Design: A Systematic Approach, ed. Wallace, K. and Blessing, L.., London: Springer-Verlag. https://doi.org/10.1007/978-1-84628-319-2CrossRefGoogle Scholar
Parraguez, P. and Maier, A. (2017), “Data-driven engineering design research: opportunities using open data”, in Proceedings of the 21st International Conference on Engineering Design (ICED 17), Vol. 7: Design Theory and Research Methodology, Vancouver, Canada, 21-25.08.2017, pp. 41-50. ISBN: 978-1-904670-95-7Google Scholar
Pennington, J., Socher, R. and Manning, C. D. (2014), “Glove: Global vectors for word representation”, Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp. 1532-1543. https://doi.org/10.3115/v1/D14-1162CrossRefGoogle Scholar
Ulrich, K.T. (2003), Product design and development, Tata McGraw-Hill Education.Google Scholar
Yu, J., Wang, G., Ming, Z., Yan, Y. and Lan, X. (2018), “Ontology-based unified representation of dynamic simulation models in engineering design”, in Proceedings of the ASME Design Engineering Technical Conference, Vol. 2A-2018. https://doi.org/10.1115/DETC2018-85536Google Scholar
Zhao, S., Xu, C. and Wang, R. (2020), “Knowledge structure generation and modularization based on binary matrix factorization in engineering design”, Journal of Mechanical Science and Technology, Vol. 34, pp. 4657–4673. https://doi.org/10.1007/s12206-020-1024-4CrossRefGoogle Scholar