No CrossRef data available.
Article contents
DATA-DRIVEN CREATIVITY: COMPUTATIONAL PROBLEM-EXPLORING IN ENGINEERING DESIGN
Published online by Cambridge University Press: 27 July 2021
Abstract
Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Creativity is required in engineering design. It is required in the aspects of problem-solving - conceptualizing a new solution to a problem, and problem-exploring - conceptualizing a new problem. Studies show that, in both aspects, creativity is a difficult task in practice. The aim of this study is to support the engineering design community by easing the difficulty in the problem-exploring practice. To achieve this, a computational problem-exploring (CPE) model is developed to mimic how design engineers identify a valid design problem. Consequently, a CPE tool - Pro-Explora V1 is developed based on the CPE model. The CPE model consists of a synergy of emergent computational technologies including data retrieval and machine learning. A Markovian model is employed in the CPE model to enable a data-driven random process for exploring design problems. In pilot test, Pro-Explora V1 generated some engineering design-related problems which are meaningful, unique, and could not be distinguished from naturally generated ones. It provides support to design engineers in problem-exploring at the early stage in engineering design. This study contributes to the global effort towards data-driven processes in the fourth industrial revolution.
- Type
- Article
- Information
- Creative Commons
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
Alter, S. and Dennis, A.R. (2002), “Selecting research topics: Personal experiences and speculations for the future”, Communications of the Association for Information Systems, Vol. 8 No.1 pp. 21.CrossRefGoogle Scholar
Bahadoran, Z., Mirmiran, P., Kashfi, K. and Ghasemi, A. (2019), “The principles of biomedical scientific writing”, International Journal of Endocrinology and Metabolism, Vol. 17 No.4.CrossRefGoogle ScholarPubMed
Bloomberg, J. (2018), Digitization, digitalization, and digital transformation: confuse them at your peril. [Online] Forbes. https://www.forbes.com (17-09-2020).Google Scholar
Boden, M.A. (2004), The creative mind: Myths and mechanisms, Routledge, New York.CrossRefGoogle Scholar
Celik, Y. (2019), Rules for Doing and Managing Research Project. [Online] Google Scholar. https://dergi.biruni.edu.tr/wp-content/uploads/2019/12/2-Yusuf-Celik.pdfGoogle Scholar
Charette, M. (2017), “Woods Hole Sea Grant in the 21st Century: Issues, Opportunities, Action for Massachusetts 2018-2021”,Google Scholar
Chiarello, F., Bonaccorsi, A. and Fantoni, G. (2020), “Technical sentiment analysis. Measuring advantages and drawbacks of new products using social media”, Comput.Ind., Vol. 123 pp. 103299.CrossRefGoogle Scholar
Cohen, M.D., March, J.G. and Olsen, J.P. (1972), “A garbage can model of organizational choice”, Adm.Sci.Q.CrossRefGoogle Scholar
Colton, S. and Wiggins, G.A. (2012), “Computational creativity: The final frontier?”, Ecai, Montpelier, pp.21–26.Google Scholar
Crawford, M. (2018), How Industry 4.0 Impacts Engineering Design. [Online] The American Society of Mechanical Engineers. https://www.asme.org (05-10-2020).Google Scholar
Dennis, A.R. and Valacich, J.S. (2001), “Conducting experimental research in information systems”, Communications of the association for information systems, Vol. 7 No.1 pp. 5.CrossRefGoogle Scholar
Einstein, A. and Infeld, L. (1938), The evolution of physics, Simon & Schuster, New York.Google Scholar
Fink, G.A. (2014), Markov models for pattern recognition: from theory to applications, Springer, London.CrossRefGoogle Scholar
Fischer, G. (1994), “Turning breakdowns into opportunities for creativity”, Knowledge-Based Syst., Vol. 7 No.4 pp. 221–232. https://doi.org/10.1016/0950-7051(94)90033-7.CrossRefGoogle Scholar
Gangopadhyay, D. (2014), “The dawn of the creative age: Fostering creativity among engineering students”, Teaching Innovation Projects, Vol. 4 No.1.Google Scholar
Getzels, J.W. (1979), “Problem finding: A theoretical note”, Cognitive Science - A Multidisciplinary Journal, Vol. 3 No.2 pp. 167–172. https://doi.org/10.1207/s15516709cog0302_4.CrossRefGoogle Scholar
Gobble, M.M. (2018), “Digitalization, digitization, and innovation”, Research-Technology Management, Vol. 61 No.4 pp. 56–59. https://doi.org/10.1080/08956308.2018.1471280.CrossRefGoogle Scholar
Greenspan, Y.F. (2016), A guide to teaching elementary science: Ten easy steps, Brill Sense, Netherlands.CrossRefGoogle Scholar
Grigorenko, E.L. (2019), “Creativity: a challenge for contemporary education”, Comparative Education, Vol. 55 No.1 pp. 116–132.CrossRefGoogle Scholar
Hajba, G. (2018), Website Scraping with Python: Using BeautifulSoup and Scrapy, Springer Science, New York.CrossRefGoogle Scholar
Han, J., Park, D., Shi, F., Chen, L., Hua, M. and Childs, P.R. (2019), “Three driven approaches to combinational creativity: Problem-, similarity-and inspiration-driven”, Proc.Inst.Mech.Eng.Part C, Vol. 233 No.2 pp. 37310.1177/0954406217750189CrossRefGoogle Scholar
Harris, S.D. and Zeisler, S. (2002), “Weak signals: Detecting the next big thing”, The Futurist, Vol. 36 No.6.Google Scholar
Hays, J.C. (2010), “Eight recommendations for writing titles of scientific manuscripts”, Public Health Nursing, Vol. 27 No.2 pp. 101–103.CrossRefGoogle ScholarPubMed
Hecklau, F., Galeitzke, M., Flachs, S. and Kohl, H. (2016), “Holistic approach for human resource management in Industry 4.0”, Procedia Cirp, Vol. 54 No. pp. 1–6.10.1016/j.procir.2016.05.102CrossRefGoogle Scholar
Jordanous, A. (2012), “A standardised procedure for evaluating creative systems: Computational creativity evaluation based on what it is to be creative”, Cognitive Computation, Vol. 4 No.3 pp. 246–279.CrossRefGoogle Scholar
Jørgensen, U. (2006), “Engineering design competences–controversial relations between techno-science discipline and engineering practice domains pointing to new foundations for engineering knowledge”, INES Workshop, Virginia Tech, Citeseer,Google Scholar
Kaplan, D.E. (2019), “Creativity in education: Teaching for creativity development”, Psychology, Vol. 10 No.2 pp. 140–147. https://doi.org/10.4236/psych.2019.102012.CrossRefGoogle Scholar
Krulik, S. and Rudnick, J.A. (1987), Problem solving: A handbook for teachers, Allyn and Bacon, Boston.Google Scholar
Langford, C.A. and Pearce, P.F. (2019), “Increasing visibility for your work: The importance of a well-written title”, American Association of Nurse Practitioners, Vol. 31 No.4 pp. 217–218.CrossRefGoogle ScholarPubMed
Meyn, S. and Tweedie, R.L. (2009), Markov Chains and Stochastic Stability, Cambridge University Press, Cambridge. 10.1017/CBO9780511626630.CrossRefGoogle Scholar
Milisavljevic-Syed, J., Allen, J.K., Commuri, S. and Mistree, F. (2019), “Design of networked manufacturing systems for Industry 4.0”, Elsevier, pp.1016–1021. https://doi.org/10.1016/j.procir.2019.03.244.Google Scholar
Müller, D. and Trahasch, S. (2019), “Architecture of a Big Data Platform for a Semiconductor Company”, DATA ANALYTICS 2019, pp. 41.Google Scholar
NAE - National Academy of Engineering, US (2004), The engineer of 2020: Visions of engineering in the new century, National Academies Press, Washington DC.Google Scholar
Nicholl, B. and McLellan, R. (2007), “The Contribution of Product Analysis to Fixation in Students’ Design and Technology Work”, The Design and Technology Association International Research Conference 2007.Google Scholar
Nitta, K. and Satoh, K. (2021), “AI Applications to the Law Domain in Japan”, Asian Journal of Law and Society, pp. 1–24.Google Scholar
Obieke, C., Milisavljevic-Syed, J. and Han, J. (2020), “Supporting Design Problem-exploring with Emergent Technologies”, Procedia CIRP, Vol. 91 pp. 373–381. https://doi.org/10.1016/j.procir.2020.02.189.CrossRefGoogle Scholar
Oermann, M. H. and Leonardelli, A. K. (2013) 'Make the Title Count' Nurse Author &Editor Newsletter, Vol. 23Google Scholar
Plötz, T. and Fink, G.A. (2011), Markov Models for Handwriting Recognition, Springer, London.CrossRefGoogle Scholar
Plucker, J.A., Beghetto, R.A. and Dow, G.T. (2004), “Why isn't creativity more important to educational psychologists? Potentials, pitfalls, and future directions in creativity research”, Educational psychologist, Vol. 39 No.2 pp. 83–96.CrossRefGoogle Scholar
Polanyi, M. (1958), Personal Knowledge: Towards a post critical philosophy, Routledge, London.Google Scholar
Preuveneers, D. and Ilie-Zudor, E. (2017), “The intelligent industry of the future: A survey on emerging trends, research challenges and opportunities in Industry 4.0”, Journal of Ambient Intelligence and Smart Environments, Vol. 9 No.3 pp. 287–298. https://doi.org/10.3233/ais-170432.CrossRefGoogle Scholar
Privault, N. (2013), Understanding Markov Chains: Examples and Applications, Springer-Verlag, Singapore.CrossRefGoogle Scholar
Rabiner, L.R. (1989), “A tutorial on hidden Markov models and selected applications in speech recognition”, Proc IEEE, Vol. 77 No.2 pp. 257–286.CrossRefGoogle Scholar
Runco, M.A. (2014), Creativity. Theories and Themes: Research, Development, and Practice, Elsevier, London.Google Scholar
Sheskin, T.J. (2011), Markov chains and decision processes for engineers and managers, CRC Press, London.Google Scholar
Tekmen-Araci, Y. and Mann, L. (2019), “Instructor approaches to creativity in engineering design education”, Proc.Inst.Mech.Eng.Part C, Vol. 233 No.2 pp. 395–402.CrossRefGoogle Scholar
Wang, A. and Cho, K. (2019), “Bert has a mouth, and it must speak: Bert as a markov random field language model”, arXiv preprint arXiv:1902.04094.Google Scholar
Yoshioka, T., Suganuma, T., Tang, A.C., Matsushita, S., Manno, S. and Kozu, T. (2005), “Facilitation of problem finding among first year medical school students undergoing problem-based learning”, Teach.Learn.Med., Vol. 17 No.2 pp. 136–141.CrossRefGoogle ScholarPubMed
You have
Access
Open access