Data-driven design increasingly relies on sensor data, yet these thin measurements often lack the experiential context needed to explain why events occur or what users feel and need. This limits their value for human-centred design. Passive and active contextualisation are introduced to describe how meaning is produced through inference and user participation. A real-world case study using See.Sense cycling data from Melbourne shows how combining thin and thick data produces more situated understanding and actionable design insight.

Trade-off studies often use the design of experiments approach, while simulation models enable data-based product optimization by AI. This paper presents a comparison of evolutionary algorithms, reinforcement learning as well as active learning for design space exploration. Based on a real-world case study and hypervolume analysis, the performance of selected algorithms is assessed. The results highlight their ability to identify pareto fronts and provide insights to deepen the understanding of AI-driven design space exploration.

We present a geometry-based complexity factor for additive manufacturing that estimates relative printing effort of fused filament fabricated parts from STL geometry alone. A reference effort is derived by slicing thousands of parts and volume-equivalent cubes. Eight interpretable geometric metrics feed a constrained, regularised index with monotonic calibration, achieving robust test accuracy and revealing which shape features dominate structural complexity.

This design-led study explores how circular economy principles can be embedded in research prototyping while comparing environmental sensing methods. A reusable outdoor prototype, deployed in three contexts, combined embedded sensors and API data. Using proxy indicators (energy, material mass, emissions) and end-of-life planning, API sensing indicated a far lower impact, while embedded sensing offered hyper-local data. All prototype components were either reused or repurposed, demonstrating circular prototyping in practice, with findings intended as design-informing rather than definitive.

Dominant designs establish de facto standards for all products within an industry, shaping both competition and innovation dynamics. Studying dominant designs enables firms to make informed decisions for new product development and to anticipate technological shifts. This paper presents a computer-based method that automatically extracts the spatial configuration of components from patent drawings to support the analysis of dominant designs and anomaly detection. A case study on eyeglasses validates the approach, demonstrating its potential for data-driven design innovation.

This work presents an ML-based inverse design framework for multi-material lattices with curved struts, targeting mechanical and thermal performance. Using cubic-spline parameterization and discrete material assignment, the design space expands beyond conventional lattices. A workflow combining a material classifier, property predictor, and inverse generators addresses one-to-many mapping, enabling probabilistic sampling and diverse designs. The approach supports multi-objective trade-offs and lays the foundation for multi-scale optimization of functionally graded metamaterials.

AI in Requirements Engineering (RE) relies on industrial data, yet safety and privacy risks limit its use. While the GDPR distinguishes only between anonymization and pseudonymization, we use neutralization as a semantics-preserving technique. In AI-supported RE, data heterogeneity and cross-domain variability impede model training. We propose guidelines for semantics-preserving preprocessing for RE datasets based on ISO 29148 criteria, showing that neutralization does not compromise semantics. The approach enables industry–academia collaboration through AI-assisted RE in product development.

We introduce a method that turns online customer reviews into design insights. By analysing smartphone reviews, we extract the product features customers talk about and identify the sentiment linked to them. The approach combines Large Language Models (LLMs) with the Kano model, showing how specific features influence satisfaction or dissatisfaction. The results are coherent with the dimensions of the Kano model. The work demonstrates that LLMs can be informed and constrained by established design frameworks, bridging LMMs and design reasoning to provide theory-grounded insights.