Conceptual CAD requires transforming functional requirements into parametric 3D models, yet existing systems have steep learning curves and limit creativity through premature fixation. Generative AI shows promise in producing diverse alternatives, while current methods mainly reconstruct CAD modeling sequences of existing designs, making them unsuitable for early stages where ideas are vague and intent is difficult to express. We present Req2CAD, an interactive system that enables designers to progress from design problems toward conceptual CAD models through functional decomposition, function–structure reasoning, and component-level CAD creation and iteration. Req2CAD introduces a data annotation pipeline that maps functional requirements to the 3D structural design space, a dual-feature CAD representation to support design space exploration and CAD ideation, and a progressive CAD generation method that enables rapid CAD model creation through multi-modal intent expression. A technical evaluation and user study demonstrate the effectiveness of Req2CAD, highlighting its potential for human–AI co-creation.

A design method card deck helps designers understand and provoke thinking by presenting each method in a simple format and allow designers to switch between methods seamlessly by maintaining the same simple format across the deck. However, recent observations have shown designers hesitate to use a card deck due to the lack of support, while other tools have provided identified support with generative AI. Through a formative study, we identified the specific support designers need when applying the design method cards and intentions in integrating generative AI. Accordingly, we developed the intelligent design method card deck, I-Card, which integrates generative AI to provide applicable design methods, design knowledge and data support, and interactive and dynamic support. A user study demonstrates that I-Card improved the design efficiency and applicability by offering personalized guidance, enhanced decision-making with comprehensive data generation and provided more design inspiration via interactive support.

Bio-inspired design (BID) fosters innovations in engineering. Learning BID is crucial for developing multidisciplinary innovation skills of designers and engineers. Current BID education aims to enhance learners’ understanding and analogical reasoning skills. However, it often heavily relies on the teachers’ expertise. When learners pursue independent learning using some educational tools, they face challenges in understanding and reasoning practice within this multidisciplinary field. Additionally, evaluating their learning outcomes comprehensively becomes problematic. Addressing these challenges, we introduce a LLMs-driven BID education method based on a structured ontology and three strategies: enhancing understanding through LLMs-enpowered "learning by asking", assisting reasoning by providing hints and feedback, and assessing learning outcomes through benchmarking against existing BID cases. Implementing the method, we developed BIDTrainer, a BID education tool. User studies indicate that learners using BIDTrainer understood BID knowledge better, reason faster with higher interactivity than the baseline, and BIDTrainer assessed the learning outcomes consistent with experts.

Divergent thinking is a process in design by exploring multiple possible solutions, is crucial in the early stages of design to break fixation and expand the design ideation. Design-by-Analogy promotes divergent thinking, by studying solutions have solved similar problems and using this knowledge to make inferences and solve problems in new and unfamiliar situations. Bio-inspired design (BID) is a form of design by analogy and its knowledge provides diverse sources for analogy, making BID knowledge as a potential source for divergent thinking. Existing BID database has focused on collecting BID cases and facilitating the retrieval of biological knowledge. Despite its success, applying BID knowledge into divergent thinking still encounters challenge, as the association between source domain and target domain are always limited within a single case. In this work, a novel approach is proposed to support divergent thinking from three subsequent phases: encoding, retrieval and mapping. Specifically, biological knowledge is encoded in a triple form by employing a large language model (LLM) to extract key information from a well-known BID knowledge base. The created triples are implemented in a semantic network to facilitate bidirectional retrieval modes: problem-driven and solution-driven, as well as mapping for divergent thinking. The mapping algorithm calculates the semantic similarity between nodes in the semantic network based on their attributes in three progressive steps by following the paradigm of divergent thinking. The proposed approach is implemented as tool called AskNatureNet,1 which supports divergent thinking by retrieving and mapping knowledge in a visualized interactive semantic network. An ideation case study on evaluating the effectiveness of AskNatureNet shows that our tool is capable of supporting divergent thinking efficiently.

This paper shows analytical and experimental evidence of using the vibration dynamics of a compliant whisker for accurate terrain classification during steady state motion of a mobile robot. A Hall effect sensor was used to measure whisker vibrations due to perturbations from the ground. Analytical results predict that the whisker vibrations will have one dominant frequency at the vertical perturbation frequency of the mobile robot and one with distinct frequency components. These frequency components may come from bifurcation of vibration frequency due to nonlinear interaction dynamics at steady state. Experimental results also exhibit distinct dominant frequency components unique to the speed of the robot and the terrain roughness. This nonlinear dynamic feature is used in a deep multi-layer perceptron neural network to classify terrains. We achieved 85.6% prediction success rate for seven flat terrain surfaces with different textures.