If a Picture is Worth 1000 Words, Is a Word Worth 1000 Features for Design Metric Estimation?

Edwards, Kristen; Peng, Aoran; Miller, Scarlett R.; Ahmed, Faez

doi:10.1115/1.4053130

Cited by 3 publications

(1 citation statement)

References 53 publications

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“…Sketching provides a rapid external representation that comes at very little cognitive cost (Goldschmidt, 2014). Researchers also explored creativity and decision making with sketches (Toh and Miller, 2016), using sketching for finite element analysis (Murugappan et al, 2017), and using machine learning to predict creativity-ratings from sketches and text (Edwards et al, 2022;Song et al, 2023).…”

Section: Sketching and Prototyping In Engineering Designmentioning

confidence: 99%

Sketch2Prototype: rapid conceptual design exploration and prototyping with generative AI

Edwards,

Man,

Ahmed

2024

Proc. Des. Soc.

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Sketch2Prototype is an AI-based framework that transforms a hand-drawn sketch into a diverse set of 2D images and 3D prototypes through sketch-to-text, text-to-image, and image-to-3D stages. This framework, shown across various sketches, rapidly generates text, image, and 3D modalities for enhanced early-stage design exploration. We show that using text as an intermediate modality outperforms direct sketch-to-3D baselines for generating diverse and manufacturable 3D models. We find limitations in current image-to-3D techniques, while noting the value of the text modality for user-feedback.

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Section: Sketching and Prototyping In Engineering Designmentioning

confidence: 99%

Sketch2Prototype: rapid conceptual design exploration and prototyping with generative AI

Edwards,

Man,

Ahmed

2024

Proc. Des. Soc.

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Sustainable Creativity: Overcoming the Challenge of Scale When Repurposing Wind-Turbine Blades

Arabian

Shu

2022

Journal of Mechanical Design

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Increased adoption of wind-energy technology helps address climate change, but also requires disposition of retired wind-turbine blades that are not easily recycled. This pressing environmental problem is used as the prompt in a creativity study, where participants are asked to identify potential reuses in a Wind-turbine-blade Repurposing Task (WRT). In past iterations of this study, participants consistently struggled with correctly incorporating the large physical size of wind-turbine blades in their reuse concepts. The Alternate Uses Task (AUT) is an established measure of creativity and asks participants to identify uses for much smaller objects like bricks and paper clips. The current work explored whether an AUT can be adapted as an intervention to help overcome the scale challenge in the WRT. Students in a fourth-year undergraduate engineering design course (N=28) underwent both of two conditions, a scaled-AUT intervention and a control, typical AUT before the WRT. AUT fluency and flexibility (number and categories of ideas) were significantly lower in the scaled AUT than the typical AUT. This result supports that object scale more than unfamiliarity is the main WRT challenge, since the AUT objects were relatively common. Notably, correctly scaled WRT concepts significantly increased after the scaled AUT, supporting the intervention's effectiveness. Finally, the WRT is proposed as a standard design-study task whose solutions help address a real-world problem.

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Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations

Song¹,

Miller²,

Ahmed

2023

Journal of Mechanical Design

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Conceptual design evaluation is an indispensable component of innovation in the early stage of engineering design. Properly assessing the effectiveness of conceptual design requires a rigorous evaluation of the outputs. Traditional methods to evaluate conceptual designs are slow, expensive, and difficult to scale because they rely on human expert input. An alternative approach is using computational methods to evaluate design concepts. However, most existing methods have limited utility because they are constrained to unimodal design representations (e.g., texts or sketches). To overcome these limitations, we propose an attention-enhanced multimodal learning-based model to predict five design metrics: drawing quality, uniqueness, elegance, usefulness, and creativity. The proposed model utilizes knowledge from large external datasets through transfer learning, simultaneously processes text and sketch data from early-phase concepts, and effectively fuses the multimodal information through a mutual cross-attention mechanism. To study the efficacy of multimodal learning (MML) and attention-based information fusion, we compare (1) a baseline MML model and the unimodal models and (2) the attention-enhanced models with baseline models in terms of their explanatory power for the variability of the design metrics. The results show that MML improves model explanatory power by 5%-12% and the mutual cross-attention mechanism further increases the explanatory power by 5%-9%, leading to a highest explanatory power of 44% for drawing quality, 60% for uniqueness, 45% for elegance, 43% for usefulness, and 32% for creativity. Our findings highlight the benefit of using multimodal representations for design metric assessment.

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If a Picture is Worth 1000 Words, Is a Word Worth 1000 Features for Design Metric Estimation?

Cited by 3 publications

References 53 publications

Sketch2Prototype: rapid conceptual design exploration and prototyping with generative AI

Sketch2Prototype: rapid conceptual design exploration and prototyping with generative AI

Sustainable Creativity: Overcoming the Challenge of Scale When Repurposing Wind-Turbine Blades

Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations

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