The aerospace interior design industry operates under strict regulatory constraints, also requiring rapid development cycles without compromising passenger safety or comfort. Collins Aerospace initiated the project brief to explore how can AI help with the design process. The thesis project explores the main research question “How can AI integration enhance the product development design workflow at Collins Aerospace?” The topic is explored through a contextual research at Collins and three design sprints. Contextual research at Collins revealed an operational gap in the product development workflow: early-stage ergonomic user testing with human participants is rarely conducted due to high financial costs, extended timelines, corporate privacy policies and safety liabilities. As a result, conceptual ergonomic decisions often rely on subjective designer assumptions, self-testing or static industry ergonomics guidelines.

Using the Design Sprints framework (Knapp, Zeratsky, & Kowitz, 2016), it is aimed to discover the topic in depth through three research-design-evaluate cycles. Sprint I explores the AI trends across industries on product development processes alongside the company's contextual research, identifying the lack of human ergonomic user testing as a primary bottleneck. This finding shaped the scope of the project to focusing on improving the user testing aspect of the product development process. Sprint I also included the first ideation of the project. Sprint II explores AI trends and applications in user testing areas both in aviation and in similar industries. Following, the second ideation session is conducted which shaped the primary ideas for the final concept. Later the thesis introduces the final design concept: ErgoSim.

ErgoSim is an AI-augmented, digital ergonomic design review tool that functions as a digital passenger ergonomics twin, integrated directly within the designer's CAD workspace. Operating via a closed-loop 3-tier architecture—connecting a CAD Tier (Rhino/Grasshopper), a Middle Tier (Python and TU Delft DINED database), and an AI Tier (LLM feedback engine), the system extracts spatial dimensions, normalises them against P5Female to P95Male anthropometric boundary envelopes and delivers real-time, context-aware ergonomic reviews.

Later, the ErgoSim tool is tested with five TU Delft IDE students across four aspects: Usability and Navigation, Quality and Trust, Automation Bias, Workflow Integration and Perceived Value. The user evaluation confirmed that ErgoSim successfully transforms ergonomic evaluation from a late-stage review into a real-time, data-grounded co-design process. Participants praised the AI report understandability (4.6 / 5.0) and quality (4.0 / 5.0), highlighting that the AI provided flexible, advisory guidance (4.0 / 5.0) rather than forced automation, thus preserving designer autonomy. While testing identified usability friction points regarding the tool’s canvas navigation and main dashboard settings, ErgoSim demonstrates that embedding generative AI into native CAD environments accelerates iteration loops, introduces complex human factors data and provides objective ergonomics justification for early design decisions. ... Read more