隨著人工智能、大數據和雲計算技術的快速進展，各類生成式人工智能應用紛紛湧現，促成了大量的人工智能生成內容（Artificial Intelligence Generated Content, AIGC）。面對傳統線性工業設計流程，AIGC技術展現了在關鍵設計階段中提升設計生產力的巨大潛力。儘管設計產業和學術界對於AIGC持續展現出高度關注，目前學界尚缺乏關於將AIGC融入工業設計方法論的研究，也鮮少探討AIGC如何具體參與工業設計流程的各個階段。爲了革新及優化傳統的工業設計流程，本研究基於AIGC提出一個充滿創意且實用的工業設計流程模型。此設計流程模型主要由四個階段組成，即準備(Phase 1)，形態生成(Phase 2)，顔色生成(Phase 3)和配色(Phase 4)。在準備階段(In Phase 1)，文本生成式AI程序GPT-4.0被用於生成可描述目標產品形態和配色的意象形容詞，並邀請設計師和消費者篩選出幾個典型的意象形容詞作爲目標意象。在形態生成階段(In Phase 2)，GPT-4.0被用於生成Midjourney中可使用的提示詞短語，進而將目標意象形容詞和有效的提示詞短語依次輸入至Midjourney以建構一個形態樣本資料庫。隨後，透過灰關聯分析(grey relation analysis, GRA)、感性評價和曲綫二次曲率熵(quadratic curvature entropy)評價法篩選出符合目標意象的最佳形態。最後，計算機輔助設計工具Rhino被用於建構最佳形態的三維模型並優化其人機尺寸和形態元素。在顔色生成階段(In Phase 3)，基於Midjourney提出了四種生成自然界色彩圖像的方法。使用任一方法生成彩色圖像並使用Python的OpenCV模組提取出彩色圖像的主要顔色。在配色階段(In Phase 4)，基於色彩調和理論獲得每張圖像中美度最高的色彩組合，並將其應用至最佳形態以獲得一組配色備選方案。最後，計算機輔助設計工具KeyShot被用於繪製備選方案的三維渲染效果圖。隨後，使用配色美度量化公式和模糊層次分析法(FAHP)，以及消費者感性問卷評價配色備選方案，並使用皮爾森相關係數驗證評價結果之間的相關性。本研究以電動摩托車、家用吸塵器和斑海豹玩偶作為案例，驗證所提出的基於AIGC的工業設計流程模型。結果表明，生成式AI作為一種新興的設計輔助工具，能夠有效革新傳統設計流程，提升設計效率。本研究還展示了傳統計算機輔助設計工具與生成式AI程序協同工作的設計流程，為設計從業者提供了重審工業設計流程的新視角。

With the rapid development of artificial intelligence, big data, and cloud computing technologies, a variety of generative artificial intelligence applications have emerged, leading to the proliferation of Artificial Intelligence Generated Content (AIGC). Facing traditional linear industrial design processes, AIGC technology has shown great potential to enhance design productivity in key design stages. Despite the high level of attention AIGC continues to receive from the design industry and academia, there is currently a lack of research on integrating AIGC into industrial design methodologies, and the specific involvement of AIGC in various stages of the industrial design process is rarely discussed. To innovate and optimize traditional industrial design processes, this study proposes a creative and practical industrial design process model based on AIGC. This design process model is mainly composed of four stages: preparation (Phase 1), shape generation (Phase 2), color generation (Phase 3), and color matching (Phase 4). In the preparation phase (Phase 1), the text-generating AI program GPT-4.0 is used to generate descriptive adjectives that can describe the target product's shape and color matching, and designers and consumers are invited to select several typical adjectives as target imageries. In the shape generation phase (Phase 2), GPT-4.0 is used to generate prompt subphrases that can be used in Midjourney, and then the target imageries and effective prompt subphrases are entered into Midjourney to construct a shape sample database. Subsequently, through Grey Relation Analysis (GRA), perceptual evaluation, and quadratic curvature entropy evaluation methods, the optimal shape that matches the target imagery is selected. Finally, the computer-aided design tool Rhino is used to construct the three-dimensional model of the optimal shape and optimize its ergonomics and shape elements. In the color generation phase (Phase 3), four methods for generating natural world color images based on Midjourney are proposed. Any method is used to generate color images, and the Python OpenCV module is used to extract the dominant colors of the color images. In the color matching phase (Phase 4), based on color harmony theory, the most aesthetically pleasing color combinations in each image are obtained and applied to the optimal shape to obtain a set of color-matching alternatives. Finally, the computer-aided design tool KeyShot is used to draw three-dimensional rendering images of the alternatives. Subsequently, the color-matching alternatives are evaluated using a color aesthetic quantification formula and Fuzzy Analytic Hierarchy Process (FAHP), as well as consumer perceptual questionnaires, and the Pearson correlation coefficient is used to verify the correlation between the evaluation results. This study uses an electric motorcycle, a household vacuum cleaner, and a seal doll as cases to validate the proposed AIGC-based industrial design process model. The results show that generative AI, as an emerging design aid tool, can effectively innovate traditional design processes and improve design efficiency. This study also demonstrates the design process of traditional computer-aided design tools working together with generative AI programs, providing a new perspective for design practitioners to reconsider the industrial design process.

Abdel-Basset, M., Mohamed, R., Elhoseny, M., & Chang, V. (2020). Evaluation framework for smart disaster response systems in uncertainty environment. Mechanical Systems and Signal Processing, 145, 106941.
Ahmadi, S., Yeh, C. H., Papageorgiou, E. I., & Martin, R. (2015). An FCM–FAHP approach for managing readiness-relevant activities for ERP implementation. Computers & Industrial Engineering, 88, 501-517.
Alışık, E. (2022). “All Compressed and Rendered with a Pathetic Delicacy That Astounds the Eye”: Midjourney Renders Ambergris as Constantinople. CyberOrient, 16(2), 76-88.
Archer, L. B. (1984). Systematic method for designers. in: N. Cross (Ed.), Developments in Design Methodology, Wiley, Chichester.
Bahn, S., Lee, C., Nam, C. S., & Yun, M. H. (2009). Incorporating affective customer needs for luxuriousness into product design attributes. Human Factors and Ergonomics in Manufacturing & Service Industries, 19(2), 105-127.
Bala, V., Duesterwald, E., & Banerjia, S. (2000). Dynamo: A transparent dynamic optimization system, in: Proceedings of the ACM SIGPLAN 2000 Conference on Programming Language Design and Implementation, ACM, 2000, pp.1-12.
Bezdek, J.C. (2013), Pattern recognition with fuzzy objective function algorithms, Springer Science & Business Media, Berlin.
Bhandari, U., Neben, T., Chang, K., & Chua, W. Y. (2017). Effects of interface design factors on affective responses and quality evaluations in mobile applications. Computers in Human Behavior, 72, 525-534.
Binkley, M., Erstad, O., Herman, J., Raizen, S., Ripley, M., Miller-Ricci, & M., Rumble, M. (2012). Defining twenty-first century skills. in: G. Patrick (Ed.), Assessment and teaching of 21st century skills ,17-66.
Blahut, R. E. (1987). Principles and practice of information theory. Addison-Wesley Longman Publishing Co., Inc.
Borji, A. (2022). Generated faces in the wild: Quantitative comparison of stable diffusion, midjourney and dall-e 2. arXiv preprint arXiv:2210.00586.
Brown, T. (2008). Design thinking. Harvard Business Review, 86(6), 84–92.
Brown, T., & Katz, B. (2019). Change by design: How design thinking transforms organizations and inspires innovation. Retrieved from Amazon. com.
Cahan, P., & Treutlein, B. (2023). A conversation with ChatGPT on the role of computational systems biology in stem cell research. Stem Cell Reports, 18(1), 1-2.
Cai, W., Zhai, B., Liu, Y., Liu, R., & Ning, X. (2021). Quadratic polynomial guided fuzzy C-means and dual attention mechanism for medical image segmentation. Displays, 70, 102106.
Castelvecchi, D. (2022). Are ChatGPT and AlphaCode going to replace programmers?, Nature.
Chambon, P., Bluethgen, C., Langlotz, C. P., & Chaudhari, A. (2022). Adapting pretrained vision-language foundational models to medical imaging domains. arXiv preprint arXiv:2210.04133.
Chang, S.M., (2002). Observation threshold of cellular phone represented angles and its related factors. National Cheng Kung University, Department of Industrial Design.
Chechurin, L., & Borgianni, Y. (2016). Understanding TRIZ through the review of top cited publications. Computers in Industry, 82, 119-134.
Chen S. J, Yan X. A, & Xie Q. S. (1994). Product styling design principles and methods. Tianjin University Press.
Chen, D., & Cheng, P. (2020). The style design of professional female vest based on kansei engineering. International Journal of Clothing Science and Technology, 32(1), 5-11.
Chen, L., Su, W., Feng, Y., Wu, M., She, J., & Hirota, K. (2020). Two-layer fuzzy multiple random forest for speech emotion recognition in human-robot interaction. Information Sciences, 509, 150-163.
Chen, Y.C. (2017). Study of Real Estate Price Model Combining with Real Estate Transactions and Livehood Facility Information of S ix Cities in Taiwan. Shijian University, Department of Information Technology and Management.
Chu, C. H., Wang, I. J., Wang, J. B., & Luh, Y. P. (2017). 3D parametric human face modeling for personalized product design: Eyeglasses frame design case. Advanced Engineering Informatics, 32, 202-223.
Cross, N. (2021). Engineering design methods: strategies for product design (Fourth Edition). John Wiley & Sons.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297-334.
Denning, P. J. (2013). Design thinking. Communications of the ACM, 56(12), 29-31.
Dorst, K. (1997). Describing design: A comparison of paradigms. Technische Universiteit Delft Press.
Dorst, K. (2015). Frame innovation: Create new thinking by design. MIT Press.
Deng, J. L. (1982). Control problems of grey systems. Systems & control letters, 1(5), 288-294.
Dorst, K., & Dijkhuis, J. (1995). Comparing paradigms for describing design activity. Design Studies, 16(2), 261–274.
Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3), 32-57.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., & Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.
Editorials, N. (2023). Tools such as ChatGPT threaten transparent science; here are our ground rules for their use. Nature, 613(612), 10-1038.
Fraser, K. C., Kiritchenko, S., & Nejadgholi, I. (2023). A Friendly Face: Do Text-to-Image Systems Rely on Stereotypes when the Input is Under-Specified?. arXiv preprint arXiv:2302.07159.
Goetschalckx, L., Andonian, A., Oliva, A., & Isola, P. (2019). Ganalyze: Toward visual definitions of cognitive image properties. In Proceedings of the IEEE/CVF International Conference on Computer Vision (PP. 5744-5753).
Gong, S. M., Lee, & W. Y. (2019). Colour harmony of two-colour combinations using a 3D colour configuration. Coloration Technology, 135(4), 292-304.
Guo, F., Li, M., Hu, M., Li, F., & Lin, B. (2019). Distinguishing and quantifying the visual aesthetics of a product: an integrated approach of eye-tracking and EEG. International Journal of Industrial Ergonomics, 71, 47-56.
Hao, Y., Zhang, J., Liu, W., & Goh, M. (2022). New enhanced clustering algorithms for patient referrals in medical consortia. Computers & Industrial Engineering, 169, 108257.
Harada, T., Yoshimoto, F., Moriyama, M. (1999). An aesthetic curve in the field of industrial design. In Proceedings 1999 IEEE Symposium on Visual Languages, Tokyo, Japan, pp. 38-47.
Hartono, M., & Chuan, T. K. (2011). How the Kano model contributes to Kansei engineering in services. Ergonomics, 54(11), 987-1004.
Ho, J., Chan, W., Saharia, C., Whang, J., Gao, R., Gritsenko, A., & Salimans, T. (2022). Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.02303.
Hollstrand, C. H., & Nordlund, K. N. (2020). The dreamcatcher-An innovative tool focusing on possibilities instead of limitations, Ann. Rheum. Dis. 79 (2020) 1290-1290.
Horváth, I., & Vroom, R. W. (2015). Ubiquitous computer aided design: A broken promise or a Sleeping Beauty?. Computer-Aided Design, 59, 161-175.
Hsiao, K. L., & Chen, C. C. (2018). What drives smartwatch purchase intention? Perspectives from hardware, software, design, and value. Telematics and Informatics, 35(1), 103-113.
Hsiao, S. W., & Tsai, C. J. (2015). Transforming the natural colors of an image into product design: A computer-aided color planning system based on fuzzy pattern recognition. Color Research & Application, 40(6), 612-625.
Hsiao, S. W., Chiu, F. Y., & Hsu, H. Y. (2008). A computer‐assisted colour selection system based on aesthetic measure for colour harmony and fuzzy logic theory. Color Research & Application, 33(5), 411-423.
Hsiao, S. W., & Tsai, H. C. (2005). Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design. International journal of industrial ergonomics, 35(5), 411-428.
Hsiao, S. W., & Yang, M. H. (2017). A methodology for predicting the color trend to get a three-colored combination. Color Research & Application, 42(1), 102-114.
Hsiao, S. W., & Chiu, F. Y., Lu, S. H. (2010). Product-form design model based on genetic algorithms. International Journal of Industrial Ergonomics, 40(3), 237-246.
Hsiao, S. W., Chiu, F. Y., & Chen, C. S. (2008). Applying aesthetics measurement to product design. International Journal of Industrial Ergonomics, 38(11-12), 910-920.
Hsiao, S. W., Yang, M. H., & Lee, C. H. (2017). An aesthetic measurement method for matching colours in product design. Color Research & Application, 42(5), 664-683.
Hsiao, S. W., Lin, H. H., & Ko, Y. C. (2017). Application of grey relational analysis to decision-making during product development. Eurasia Journal of Mathematics, Science and Technology Education, 13(6), 2581-2600.
Hsu, M. C., Wang, C., & Herrema, A. J., Schillinger, D., Ghoshal, A., Bazilevs, Y. (2015). An interactive geometry modeling and parametric design platform for isogeometric analysis. Computers & mathematics with applications, 70(7), 1481-1500.
Huang, M., Cui, G., Melgosa, M., García, P. A., Liu, H., Liu, Y., & Luo, M. R. (2017). Color harmony in two-piece garments. Color Research & Application, 42(4), 498-511.
Hunt, K. M. (2023). Could artificial intelligence win the next Weather Photographer of the Year competition?. Weather, 78(4), 108-112.
Ilevbare, I. M., Probert, D., & Phaal, R. (2013). A review of TRIZ, and its benefits and challenges in practice. Technovation, 33(2-3), 30-37.
Ishihara, S., Ishihara, K., Nagamachi, M., & Matsubara, Y. (1995). An automatic builder for a Kansei Engineering expert system using self-organizing neural networks. International Journal of Industrial Ergonomics, 15(1), 13-24.
Jian, S. Q. (2011). Industrial design methodology. Beijing Institute of Technology Press.
Jo, A. (2023). The promise and peril of generative AI. Nature, 614(1), 214-216.
Jones, J. C. (1984). A method of systematic design. in: N. Cross (Ed.), Developments in Design Methodology, Wiley, Chichester.
Joung, J., & Kim, H. M. (2021). Approach for importance–performance analysis of product attributes from online reviews. Journal of Mechanical Design, 143(8), 081705.
Judd, D. B. (1952). Color in business, science and industry: Wiley. New York.
Khalid, H. M. (2006). Embracing diversity in user needs for affective design. Applied ergonomics, 37(4), 409-418.
Kim, J. (2012). The institutionalization of YouTube: From user-generated content to professionally generated content. Media, culture & society, 34(1), 53-67.
Kumar, N., & Kumar, H. (2022). A fuzzy clustering technique for enhancing the convergence performance by using improved Fuzzy c-means and Particle Swarm Optimization algorithms. Data & Knowledge Engineering, 140, 102050.
Lai, X., Zhang, S., Mao, N., Liu, J., & Chen, Q. (2022). Kansei engineering for new energy vehicle exterior design: An internet big data mining approach. Computers & Industrial Engineering, 165, 107913.
Law, D., Cheung, M. C., Yip, J., Yick, K. L., & Wong, C. (2017). Scoliosis brace design: influence of visual aesthetics on user acceptance and compliance. Ergonomics, 60(6), 876-886.
Li, C., Pan, H., Bousseau, A., & Mitra, N. J. (2020). Sketch2cad: Sequential cad modeling by sketching in context. ACM Transactions on Graphics (TOG), 39(6), 1-14.
Li, X., Xie, C., & Sha, Z. (2022). A Predictive and Generative Design Approach for Three-Dimensional Mesh Shapes Using Target-Embedding Variational Autoencoder. Journal of Mechanical Design, 144(11), 114501.
Lian, X., Liu, Y., Bu, X., & Hou, L. (2023). Combined forecasting approach for product quality based on support vector regression and gray forecasting model. Advanced Engineering Informatics, 57, 102070.
Liebrenz, M., Schleifer, R., Buadze, A., Bhugra, D., & Smith, A. (2023). Generating scholarly content with ChatGPT: ethical challenges for medical publishing. The Lancet Digital Health, 5(3), e105-e106.
Liedtka, J. (2015). Perspective: Linking design thinking with innovation outcomes through cognitive bias reduction. Journal of Product Innovation Management, 32(6), 925-938.
Lin, K. Y., Chien, C. F., & Kerh, R. (2016). UNISON framework of data-driven innovation for extracting user experience of product design of wearable devices. Computers & Industrial Engineering, 99, 487-502.
Lin, S., & Hanrahan, P. (2013). Modeling how people extract color themes from images. in: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 3101-3110).
Liu, S. F., Hsu, Y. C., & Tsai, H. C. (2019). Development of a new cultural design process using Kansei engineering and fuzzy techniques: A case study in Mazu crown design. International Journal of Clothing Science and Technology, 31(5), 663-684.
Liu, V., & Chilton, L. B. (2022). Design guidelines for prompt engineering text-to-image generative models. in: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-23).
Lo, C. H., Ko, Y. C., & Hsiao, S. W. (2015). A study that applies aesthetic theory and genetic algorithms to product form optimization. Advanced Engineering Informatics, 29(3), 662-679.
Lu, P., & Hsiao, S. W. (2022). A product design method for form and color matching based on aesthetic theory. Advanced Engineering Informatics, 53, 101702.
Lu, P., Hsiao, S. W., & Wu, F. (2021). A product shape design and evaluation model based on morphology preference and macroscopic shape information. Entropy, 23(6), 639.
Lu, P., Li, Z., Li, J., & Hsiao, S. W. (2021). A consumer-oriented design thinking model for product design education. Interactive Learning Environments, 1-19.
Lyu, Y., Wang, X., Lin, R., & Wu, J. (2022). Communication in human-AI co-creation: Perceptual analysis of paintings generated by text-to-image system. Applied Sciences, 12(22), 11312.
Ly, A., Marsman, M., & Wagenmakers, E. J. (2018). Analytic posteriors for Pearson's correlation coefficient. Statistica Neerlandica, 72(1), 4-13.
March, L. J. (1984). The logic of design. in: N. Cross (Ed.), Developments in Design Methodology, Wiley, Chichester.
Micheli, P., Wilner, S. J., Bhatti, S. H., Mura, M., & Beverland, M. B. (2019). Doing design thinking: Conceptual review, synthesis, and research agenda. Journal of Product Innovation Management, 36(2), 124-148.
Moody, S. (1980). The role of industrial design in technological innovation. Design studies, 1(6), 329-339.
Moon, P., & Spencer, D. E. (1944). Aesthetic measure applied to color harmony. Journal of the Optical of America, 34(4), 234-242.
Morán, J., Granada, E., Míguez, J. L., & Porteiro, J. (2006). Use of grey relational analysis to assess and optimize small biomass boilers. Fuel processing technology, 87(2), 123-127.
Mount, M., Round, H., & Pitsis, T. S. (2020). Design thinking inspired crowdsourcing: Toward a generative model of complex problem solving. California Management Review, 62(3), 103-120.
Nagamachi, M. (1995). Kansei engineering: a new ergonomic consumer-oriented technology for product development. International Journal of industrial ergonomics, 15(1), 3-11.
Nagamachi, M. (2002). Kansei engineering in consumer product design. Ergonomics in Design, 10(2), 5-9.
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., & Chen, M. (2021). Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741.
Nock, R., & Nielsen, F. (2006). On weighting clustering. IEEE transactions on pattern analysis and machine intelligence, 28(8), 1223-1235.
Oh, S., Jung, Y., Kim, S., Lee, I., & Kang, N. (2019). Deep generative design: Integration of topology optimization and generative models. Journal of Mechanical Design, 141(11), 111405.
Orsborn, S., Cagan, J., & Boatwright, P. (2009). Quantifying aesthetic form preference in a utility function. Journal of Mechanical Design, 131(6), 061001.
Oxman, R. (2017). Thinking difference: Theories and models of parametric design thinking. Design Studies, 52, 4-39.
Pahl, G., & Beitz, W. (1984). Engineering design, The design council. London, Springer, 12, 221-226.
Park, J., Abdel-Aty, M., Wu, Y., & Mattei, I. (2018). Enhancing in-vehicle driving assistance information under connected vehicle environment. IEEE Transactions on Intelligent Transportation Systems, 20(9), 3558-3567.
Park, J., Han, S. H., Kim, H. K., Cho, Y., & Park, W. (2013). Developing elements of user experience for mobile phones and services: survey, interview, and observation approaches. Human Factors and Ergonomics in Manufacturing & Service Industries, 23(4), 279-293.
Plattner, H., Meinel, C., & Leifer, L. (2012). Design thinking research. Springer-Verlarg Press.
Radhakrishnan, S., Bharadwaj, V., Manjunath, V., & Srinath, R. (2018). Creative intelligence–automating car design studio with generative adversarial networks (GAN), in: International Cross-Domain Conference for Machine Learning and Knowledge Extraction (pp. 160-175).
Reynolds, L., & McDonell, K. (2021). Prompt programming for large language models: Beyond the few-shot paradigm, in: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-7).
Rodriguez-Diaz, C. A., Jimenez, S., Bejarano, D., Bernal-Chávez, J. A., & Gelbukh, A. (2023). Measuring semantic gap between user-generated content and product descriptions through compression comparison in e-commerce. Information Sciences, 638, 118953.
Roose, K. (2022). An A.I.-Generated picture won an art prize. Artists aren’t happy. The New York Times.https://www.nytimes.com/2022/09/02/technology/ai-artificial-intelligence-artists.html.
Rospigliosi, P. A. (2023). Artificial intelligence in teaching and learning: what questions should we ask of ChatGPT?. Interactive Learning Environments, 31(1), 1-3.
Saaty, R. W. (1987). The analytic hierarchy process-what it is and how it is used. Mathematical modelling, 9(3-5), 161-176.
Sampathkumar, S., & Rajeswari, R. (2022). An automated crop and plant disease identification scheme using cognitive fuzzy c-means algorithm. IETE Journal of Research, 68(5), 3786-3797.
Sefidian, A. M., & Daneshpour, N. (2019). Missing value imputation using a novel grey based fuzzy c-means, mutual information based feature selection, and regression model. Expert Systems with Applications, 115, 68-94.
Shannon, C. E. (1948). A mathematical theory of communication. The Bell system technical journal, 27(3), 379-423.
Shieh, M. D., & Yeh, Y. E. (2013). Developing a design support system for the exterior form of running shoes using partial least squares and neural networks. Computers & Industrial Engineering, 65(4), 704-718.
Shieh, M. D., & Yeh, Y. E. (2015). A comparative study on perceptual evaluations of sports shoe exterior colors in Taiwan. Color Research & Application, 40(2), 178-193.
Shieh, M. D., Li, Y., & Yang, C. C. (2018). Comparison of multi-objective evolutionary algorithms in hybrid Kansei engineering system for product form design. Advanced Engineering Informatics, 36, 31-42.
Shivegowda, M. D., Boonyasopon, P., Rangappa, S. M., & Siengchin, S. (2022). A review on computer-aided design and manufacturing processes in design and architecture. Archives of Computational Methods in Engineering, 29(6), 3973-3980.
Smith, R. P., & Morrow, J. A. (1999). Product development process modeling. Design studies, 20(3), 237-261.
Spreafico, C. (2022). Can TRIZ (Theory of Inventive Problem Solving) strategies improve material substitution in eco-design?. Sustainable Production and Consumption, 30, 889-915.
Stef, I. D., Draghici, G., & Draghici, A. (2013). Product design process model in the digital factory context. Procedia technology, 9, 451-462.
Stickdorn, M., & Schneider, J. (2010). This is service design thinking: Basics, tools, cases. Amsterdam.
Stöckl, A. (2023). Evaluating a synthetic image dataset generated with stable diffusion. arXiv preprint arXiv:2211.01777.
Sun, L., Chen, P., Xiang, W., Chen, P., Gao, W. Y., & Zhang, K. J. (2019). SmartPaint: a co-creative drawing system based on generative adversarial networks. Frontiers of Information Technology & Electronic Engineering, 20(12), 1644-1656.
Sutherland, I. E. (1964). Sketch pad a man-machine graphical communication system. In Proceedings of the SHARE design automation workshop, pp. 6-329.
Tan, C., & Yang, S. (2021). Automatic Extraction of Color Features from Landscape Images Based on Image Processing. Traitement du Signal, 38(3).
Tan, W. R., Chan, C. S., Aguirre, H. E., & Tanaka, K. (2017). ArtGAN: Artwork synthesis with conditional categorical GANs, In: 2017 IEEE International Conference on Image Processing (pp. 3760-3764).
Tao, W., Gao, S., & Yuan, Y. (2023). Boundary crossing: an experimental study of individual perceptions toward AIGC. Frontiers in Psychology, 14, 1185880.
Terninko, J., Zusman, A., & Zlotin, B. (1998). Systematic innovation: an introduction to TRIZ (theory of inventive problem solving). CRC press.
Thorp, H. H. (2023). ChatGPT is fun, but not an author. Science, 379(6630), 313-313.
Tsai, H. C., & Chou, J. R. (2007). Automatic design support and image evaluation of two-coloured products using colour association and colour harmony scales and genetic algorithm. Computer-Aided Design, 39(9), 818-828.
Tschimmel, K. (2011). Design as a perception-in-action process. Springer-Verlag Press.
Ujiie, Y., Kato, T., Sato, K., & Matsuoka, Y. (2012). Curvature entropy for curved profile generation. Entropy, 14(3), 533-558.
Ulhaq, A., Akhtar, N., & Pogrebna, G. (2022). Efficient diffusion models for vision: A survey. arXiv preprint arXiv:2210.09292.
Van Boeijen, A., Daalhuizen, J., Van Der Schoor, R., & Zijlstra, J. (2014). Delft design guide: Design strategies and methods. Delft University of Technology Press.
Visser, W. (2006). The cognitive artifacts of designing. CRC Press.
Wadia, A. P., & McAdams, D. A. (2010). Developing biomimetic guidelines for the highly optimized and robust design of complex products or their components. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. 307-321).
Wallas, G. (1926). The art of thought. Harcourt Press.
Wang, C. C., Yang, C. H., Wang, C. S., Chang, T. R., & Yang, K. J. (2016). Feature recognition and shape design in sneakers. Computers & Industrial Engineering, 102, 408-422.
Wang, L., & Wu, C. (2020). Dynamic imbalanced business credit evaluation based on Learn++ with sliding time window and weight sampling and FCM with multiple kernels. Information Sciences, 520, 305-323.
Wang, Z. J., Montoya, E., Munechika, D., Yang, H., Hoover, B., & Chau, D. H. (2022). Diffusiondb: A large-scale prompt gallery dataset for text-to-image generative models. arXiv preprint arXiv:2210.14896.
Watson, M., Leary, M., & Brandt, M. (2022). Generative design of truss systems by the integration of topology and shape optimisation. The International Journal of Advanced Manufacturing Technology, 118(3-4), 1165-1182.
Wei, S. T., Ou, L. C., Ronnier Luo, M., & Hutchings, J. (2015). Psychophysical models of consumer expectations and colour harmony in the context of juice packaging. Color Research & Application, 40(2), 157-168.
Weng, L., Zhang, Q., Lin, Z., & Wu, L. (2021). Harnessing heterogeneous social networks for better recommendations: A grey relational analysis approach. Expert Systems with Applications, 174, 114771.
Willis, K. D., Pu, Y., Luo, J., Chu, H., Du, T., Lambourne, J. G., & Matusik, W. (2021). Fusion 360 gallery: A dataset and environment for programmatic cad construction from human design sequences. ACM Transactions on Graphics (TOG), 40(4), 1-24.
Wu, J., Qian, X., & Wang, M. Y. (2019). Advances in generative design. Comput. Aided Des, 116, 102733.
Wu, Y., Ma, L., Yuan, X., & Li, Q. (2023). Human–machine hybrid intelligence for the generation of car frontal forms. Advanced Engineering Informatics, 55, 101906.
Wu, F., Lin, Y. C., & Lu, P. (2022). Research on the design strategy of healing products for anxious users during COVID-19. International journal of environmental research and public health, 19(10), 6046.
Yan, S., Xu, S., & Zhang, S. (2023). Flexible neural color compatibility model for efficient color extraction from image. Color Research & Application.
Yang, C. C., & Shieh, M. D. (2010). A support vector regression based prediction model of affective responses for product form design. Computers & Industrial Engineering, 59(4), 682-689.
Yi, S., & Wu, C. F. (2021). Green-Extension Design-A New Strategy to Reduce the Environmental Pressure from the Existing Consumer Electronics. International Journal of Environmental Research and Public Health, 18(18), 9596.
Yoo, S., Lee, S., Kim, S., Hwang, K. H., Park, J. H., & Kang, N. (2021). Integrating deep learning into CAD/CAE system: generative design and evaluation of 3D conceptual wheel. Structural and Multidisciplinary Optimization, 64(4), 2725-2747.
Zabotto, C. N., Amaral, D. C., Hornos, C. J. M., & Benze, B. G. (2019). Automatic digital mood boards to connect users and designers with kansei engineering. International Journal of Industrial Ergonomics, 74, 102829.
Zhou, B., & Zhao, Z. (2022). A hybrid fuzzy-neural-based dynamic scheduling method for part feeding of mixed-model assembly lines. Computers & Industrial Engineering, 163, 107794.