Skip to main content Accessibility help
×
Home
Hostname: page-component-684899dbb8-489z4 Total loading time: 0.268 Render date: 2022-05-28T12:01:03.280Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true }

How Do Digital Engineering and Included AI Based Assistance Tools Change the Product Development Process and the Involved Engineers

Published online by Cambridge University Press:  26 July 2019

Abstract

HTML view is not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Current trends in product development are digital engineering, the increasing use of assistance tools based on artificial intelligence and in general shorter product lifecycles. These trends and new tools strongly rely on available data and will irreversibly change established product development processes. One example for such a new data driven tool is the plausibility check of linear finite element simulations with Convolutional Neural Networks (CNN). This tool is capable of determining whether new simulation results are plausible or non-plausible according to numeric input data. The digitalization and the increased use of data driven tools employing algorithms known from Artificial Intelligence also shifts the roles of many involved engineers. This paper describes and highlights this transition from current product development processes to a data driven / simulation driven product development process. Particularly, the shifts and changes of different roles and domains are illustrated and an example for changing roles in the design and simulation department is described. Furthermore, required adjustments in the design process are derived and compared to the current status.

Type
Article
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) 2019

References

Aggarwal, C. (2015), Data Mining. The Textbook. Springer International Publishing, New York https://doi.org/10.1007/978-3-319-14142-8Google Scholar
Bohn, B., Garcke, J., Iza-Teran, R., Paprotny, A., Peherstorfer, B., Schepsmeier, U. and Thole, C.A. (2013), “Analysis of Car Crash Simulation Data with Nonlinear Machine Learning Methods”, International Conference on Computational Science, ICCS 2013, Procedia Computer Science, pp. 621630.CrossRefGoogle Scholar
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. and Wirth, R. (1999), CRISP-DM 1.0. Step-by-step data mining guide, CRISP-DM consortium.Google Scholar
Cleve, J. and Laemmel, U. (2016), Data Mining. De Gruyter, Berlin https://doi.org/10.1515/9783110456776CrossRefGoogle Scholar
Davenport, T.H. and Patil, D.J. (2012), Data scientist: The sexiest Job of the 21st century. Harvard Business Review, pp. 7076.Google Scholar
Ehrlenspiel, K. and Meerkamm, H. (2013), Integrierte Produktentwicklung. Denkabläufe, Methodeneinsatz, Zusammenarbeit. Carl Hanser Verlag, München https://doi.org/10.3139/9783446421578CrossRefGoogle Scholar
Han, J., Kamber, M. and Pei, J. (2012), Data Mining. Concepts and Techniques. Morgen Kaufmann, Waltham https://doi.org/10.1016/C2009-0-61819-5CrossRefGoogle Scholar
IBM Corporation. (2012), “IBM SPSS Modeler CRISP-DM Handbuch”. IBM Corporation. Available at: ftp://public.dhe.ibm.com/software/analytics/spss/documentation/modeler/15.0/de/CRISP-DM.pdf (11.06.2018 - 15:36)Google Scholar
Kestel, P. and Wartzack, S. (2015), “Konzept für ein wissensbasiertes FEA-Assistenzsystem zur Unterstützung konstruktionsbegleitender Simulationen”, DfX Symposium, Herrsching, 2015, 10.07. - 10.08.2015, Tutech Verlag, Hamburg, pp. 8798.Google Scholar
Kestel, P., Schneyer, T. and Wartzack, S. (2016), “Feature-based approach for the automated setup of accurate, design-accompanying Finite Element Analyses”. 14th International Design Conference. Dubrovnik, 05.16.2016 – 05.19.2016, pp. 697706.Google Scholar
Kestel, P. and Wartzack, S. (2016), “Wissensbasierter Aufbau konstruktions-begleitender Finite-Element-Analysen durch FEA-Assistenzsystem”, Entwerfen Entwickeln Erleben, Dresden Germany, 06.30. – 07.01.2016, TUDpress, Dresden, pp. 315329.Google Scholar
Larose, D. (2006), Data Mining Methods and Models. John Wiley and Sons, Hoboken, New Jersey https://doi.org/10.1002/0471756482.indexGoogle Scholar
Runkler, T. (2010), Data-Mining. Methoden und Algorithmen intelligenter Datenanalyse. Vieweg + Teubner, Wiesbaden https://doi.org/10.1007/978-3-8348-9353-6Google Scholar
Spruegel, T.C. and Wartzack, S. (2016), “Das FEA-Assistenzsystem - Analyseteil FEdelM”, Entwerfen Entwickeln Erleben, Dresden Germany, 06.30. – 07.01.2016, TUD press, Dresden, pp. 463474.Google Scholar
Spruegel, T.C., Rothfelder, R., Bickel, S., Grauf, A., Sauer, C., Schleich, B. and Wartzack, S. (2018), “Methodology for plausibility checking of structural mechanics simulations using Deep Learning on existing simulation data”, NordDesign 2018, Linköping Sweden, 08.14. – 08.17.2018, LiU Tryck, Linköping, Session 1A Machine Learning.Google Scholar
Tan, P.-N., Steinbach, M. and Kumar, V. (2006), Introduction to Data Mining. Pearson/ Addison-Wesley, Boston.Google Scholar
Trage, S., Saier, M., Amadori, D. and Reschke, K. (2018), “Whitepaper Innovationen wie am Fließband – Auswirkungen der Digitalisierung auf die Innovation und Entwicklung von Produkten in Fertigungsunternehmen” [online] KPMG. Available at: www.hub.kpmg.de (11.06.2018 - 16:56).Google Scholar
Wirth, R. and Hipp, J. (2000), “CRISP-DM: Towards a standard process model for data mining”, 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, Manchester, pp. 2939.Google Scholar
Wolf, C. and Hennig, B. (2010), Handbuch der sozialwissenschaftlichen Datenanalyse. Springer, Wiesbaden https://doi.org/10.1007/978-3-531-92038-2Google Scholar
Vajna, S., Weber, C., Zeman, K., Hehenberger, , Gerhard, D. and Wartzack, S. (2018), CAx für Ingenieure: Eine praxisbezogene Einführung. Springer, Berlin https://doi.org/10.1007/978-3-662-54624-6CrossRefGoogle Scholar
You have Access
Open access
2
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

How Do Digital Engineering and Included AI Based Assistance Tools Change the Product Development Process and the Involved Engineers
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

How Do Digital Engineering and Included AI Based Assistance Tools Change the Product Development Process and the Involved Engineers
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

How Do Digital Engineering and Included AI Based Assistance Tools Change the Product Development Process and the Involved Engineers
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *