Abstract
Amid global sustainability trends and regulatory pressures, designers require more efficient and credible tools for circular material selection in early-stage product development. Current data sources are fragmented and lack transparency, impeding contextual recommendations and appearance evaluations, which creates a critical bottleneck in implementing circular design. To address this pain point, this study proposes a material selection support prototype that integrates generative AI recommendations with visual simulation. Adopting a three-phase Research-Through-Design (RTD) methodology, we have completed Phase 1. A core functional prototype was developed for preliminary concept validation at an international trade show, where we collected valuable feedback from designers and industry stakeholders. This process helped establish the tool's functional architecture and data processing logic. Initial findings indicate high industry anticipation for a tool that boosts search efficiency and provides visual previews, justifying the direction for subsequent systematic development.
Keywords
Circular Design; AI-Driven Design Decision-Making; Circular Materials
DOI
https://doi.org/10.21606/iasdr.2025.1046
Citation
Li, Y., Wu, Y.,and Hu, C.(2025) Circu.AI: Accelerating Circular Product Design and Material Decision-Making, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.1046
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 8 - Circular/Sustainable Design
Circu.AI: Accelerating Circular Product Design and Material Decision-Making
Amid global sustainability trends and regulatory pressures, designers require more efficient and credible tools for circular material selection in early-stage product development. Current data sources are fragmented and lack transparency, impeding contextual recommendations and appearance evaluations, which creates a critical bottleneck in implementing circular design. To address this pain point, this study proposes a material selection support prototype that integrates generative AI recommendations with visual simulation. Adopting a three-phase Research-Through-Design (RTD) methodology, we have completed Phase 1. A core functional prototype was developed for preliminary concept validation at an international trade show, where we collected valuable feedback from designers and industry stakeholders. This process helped establish the tool's functional architecture and data processing logic. Initial findings indicate high industry anticipation for a tool that boosts search efficiency and provides visual previews, justifying the direction for subsequent systematic development.