Abstract
Companies need to innovate quickly to adapt to the current rapidly changing market environment. Therefore, methods to support the computer-aided creative design process have become a hot research topic, and a variety of methods to support research on creative systems have been derived on the basis of the designer’s cognition. This study establishes a general parametric design cognitive model to support the computer-aided creative process of directed design. First, this model divides a large amount of stimulus knowledge into corresponding levels through considers multiple dimensions of inspiration and stimulus factors. Second, this model develops and validates a form-generating design technology to replace a designer’s hand-drawn sketches. This technology can quickly obtain many effective product 3D models to further increase the speed of creative realization. Finally, this study verifies the model through a case study. Through the analysis of the case output results, we found that the model can quickly generate a three-dimensional sketch plan that meets the desired goals. In turn, the generated results can stimulate the designer to generate broader inspiration. Therefore, the computer-supported creative generation model established by this research has a certain degree of scientificity and feasibility. Its novelty lies in the method can liberate the designer’s labor to a certain extent and replace the designer in completing the directed creative generation process and the plan sketch process. And the verification process reflects certain cognitive mechanisms of the human brain, Therefore, this method can be applied to some specific design propositions.
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References
Akın Ö (2013) Creativity in puzzles, inventions, and designs: sudden mental insight phenomenon. Springer, New York
Alcaide-Marzal J, Diego-Mas JA, Acosta-Zazueta G (2020) A 3D shape generative method for aesthetic product design. Des Stud 66(January 2020):144–176
Althuizen N, Reichel A (2016) The effects of IT-enabled cognitive stimulation tools on creative problem solving: a dual pathway to creativity. J Manag Inf Syst 33(1):11–44
Althuizen N, Wierenga B (2014) Supporting creative problem solving with a case-based reasoning system. J Manag Inf Syst 31(1):309–340
Appio FP, Achiche S, Martini A, Beaudry C (2017) On designers use of biomimicry tools during the new product development process: an empirical investigation. Tech Anal Strat Manag 29(7):775–789
Aqeel, Bin A (2015) Development of visual aspect of Porsche brand using CAD technology. Procedia Technology 20:170–177
Baldussu A, Cascini G (2015) About integration opportunities between TRIZ and biomimetics for inventive design. Procedia Engineering 131:3–13
Becattini N, Borgianni Y, Cascini G, Rotini F (2012) Model and algorithm for computer-aided inventive problem analysis. Comput Aided Des 44(10):961–986
Benami O, Jin Y (2002) Creative stimulation in conceptual design. In: Proc. ASME Design Engineering Technical Conferences and Computer and Information in Engineering Conference, vol 4, pp 1–13
Bernal M, Haymaker JR, Eastman C (2015) On the role of computational support for designers in action. Design Studies 41(NOV.PT.B):163–182
Chen X et al (2019) The effect of precisely defined associative distance and stimulus acquisition mode in individual creativity support systems. Behav Inform Technol:1–11
Daniel M, OrcID A, Utku G (2021) A unique transdisciplinary engineering-based integrated approach for the Design of Temporary Refugee Housing Using Kano, HOQ/QFD, TRIZ, AD. ISM and DSM Tools Designs 5(2):31–37
Delhaye E, Folville A, Bastin C (2019) The impact of semantic relatedness on associative memory in aging depending on the semantic relationships between the memoranda. Exp Aging Res 45(1):1–11
Goldschmidt G (1994) On visual design thinking: the vis kids of architecture. Des Stud 15(2):158–174
Goldschmidt G (2011) Avoiding design fixation: transformation and abstraction in mapping from source to target. J Creat Behav 45:92–100
Goucher-Lambert K, Cagan J (2019) Crowdsourcing inspiration: Using crowd generated inspirational stimuli to support designer ideation. Design Studies 61(MAR.):1–29
Guo J, Mcleod PL (2014) The impact of semantic relevance and heterogeneity of pictorial stimuli on individual brainstorming: an extension of the SIAM model. Creat Res J 26(3):361–367
Han J, Park D, Shi F, Chen L, Hua M, Childs PRN (2019) Three driven approaches to combinational creativity: problem-, similarity- and inspiration-driven. Proc Inst Mech Eng C J Mech Eng Sci 233(2):373–384
Hatchuel, A. and C.F. Salgueiredo, Beyond analogy: a model of bioinspiration for creative design. Post-Print, 2016
Hatchuel A, Weil B (2003) A new approach of innovative design: an introduction to CK theory. In: The international conference on engineering design
Hender JM et al (2002) Improving group creativity: brainstorming versus non-brainstorming techniques in a GSS environment. In: Proceedings of the 34th Annual Hawaii International Conference on System Sciences
Howard TJ, Culley SJ, Dekoninck E (2008) Describing the creative design process by the integration of engineering design and cognitive psychology literature. Des Stud 29(2):160–180
Hu Z, Rao C, Tao C, Childs PRN, Zhao Y (2019) A case-based decision theory based process model to aid product conceptual design. Cluster Computing-The Journal of Networks Software Tools ANd Applications 224:10145–10162
Jin Y, Benami O (2010) Creative patterns and stimulation in conceptual design. AI EDAM 24:191–209
Julian F, Espinach X, Alcalà M et al (2016) Creativity as educational methodology in project design disciplines[C]//. In: International Technology, Education and Development Conference
Kennedy BB, Nagel JK, Bukeima A (2015) Integrating biology, design, and engineering for sustainable innovation. In: Integrated Stem Education Conference
Keshwani S, Lenau TA, Ahmed-Kristensen S, Chakrabarti A (2017) Comparing novelty of designs from biological-inspiration with those from brainstorming. J Eng Des 28(10–12):654–680
Kielarova SW, Pradujphongphet P, Bohez ELJ (2015) New Interactive-Generative Design System: Hybrid of Shape Grammar and Evolutionary Design - An Application of Jewelry Design. In: International Conference in Swarm Intelligence
Li Z, Tate D, Lane C, Adams C (2012) A framework for automatic TRIZ level of invention estimation of patents using natural language processing, knowledge-transfer and patent citation metrics. Comput Aided Des 44(10):987–1010
Martins, J.M., A. Abreu and J. Calado, The need to develop a corporate culture of innovation in a globalization context. 2018.
Mckay MCAH, Pennington AD (2006) Combining evolutionary algorithms and shape grammars to generate branded product design. Springer, Netherlands
Nagai, Y. and H. Noguchi, How Designers transform keywords into visual images, in C&C '02. 2002: New York, 118–125.
Narraway CL, Davis OSP, Lowell S, Lythgoe KA, Turner JS, Marshall S (2020) Biotic analogies for self-organising cities. Environment and Planning B 47(2):268–286
Nguyen TA, Zeng Y (2016) Effects of stress and effort on self-rated reports in experimental study of design activities. J Intell Manuf 28:1–14
Peleg O et al (2007) Differences and interactions between cerebral hemispheres when processing ambiguous words. Lect Notes Comput Sci 4840:150–157
Santanen EL, Briggs RO, Vreede GJD (2004) Causal relationships in creative problem solving: comparing facilitation interventions for ideation. J Manag Inf Syst 20(4):167–198
Sartori J, Pal U, Chakrabarti A (2010) A methodology for supporting "transfer" in biomimetic design. Artificial Intelligence for Engineering Design, Analysis & Manufacturing 24(4):483–505
Schmitt R, Köhler M, Durá JV, Diaz-Pineda J (2014) Objectifying user attention and emotion evoked by relevant perceived product components. Journal of Sensors and Sensor Systems 3(2):315–324
Shen Z, Zhang L, Li R, Liang R (2020) The effects of icon internal characteristics on complex cognition. Int J Ind Ergon 79:102990
Stones C, Cassidy T (2010) Seeing and discovering: how do student designers reinterpret sketches and digital marks during graphic design ideation. Des Stud 31(5):439–460
Troiano AL, Birtolo C (2014) Genetic algorithms supporting generative design of user interfaces: examples. Information Sciences 259(3):433–451
Turrin M, Buelow PV, Stouffs R (2011) Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms. Adv Eng Inform 25(4):656–675
Visser W (2007) Designing as construction of representations: a dynamic viewpoint in cognitive design research. Hum Comput Interact 21(1):103–152
Wang K, Nickerson JV (2019) A Wikipedia-based method to support creative idea generation: the role of stimulus relatedness. J Manag Inf Syst 36(4):1284–1312
Wissam M, Alobaidi ES (2021) An interactive evolutionary environment for creative design. Modern Mechanical Engineering 11:27–51
Yang K et al (2015) A model for computer-aided creative design based on cognition and iteration. Proc Inst Mech Eng C J Mech Eng Sci 230
Zeng Y (2012) A theoretical model of design creativity: nonlinear design dynamics and mental stress-creativity relation. Journal of Integrated Design & Process Science 16(3):65–88
Acknowledgments
We would like to thank American Journal Experts (www.aje.com) for English language editing. We would like to thank the reviewers for their constructive comments.
Funding
The project is sponsored by the National Natural Science Foundation of China (52165033) and the National Natural Science Foundation of China (51705226).
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Yang, W., Su, J., Qiu, K. et al. Supporting computer-aided product form design research with a cognitive model of the creative process. Multimed Tools Appl 81, 21619–21639 (2022). https://doi.org/10.1007/s11042-022-12119-4
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DOI: https://doi.org/10.1007/s11042-022-12119-4