Generative Design in CAD

From Raw Engineering Data to Real-Time Optimization - How is an AI model built? 🔍 This visual from Narnia Labs breaks it down beautifully, and it’s not just a generic ML pipeline. It’s specifically designed for engineering data like 3D CAD models, simulation outputs, and performance metrics. What stood out to me: → Engineering-first data pipeline: It all starts with 1D/2D/3D design or simulation data - no text or tabular shortcuts here. → AI without parameter chaos: Instead of manually defining design variables, generative models learn directly from shapes and performance outcomes. →From model to action: Once trained and tested, the AI can be deployed as an API or GUI for real-time design evaluation and optimization. 🔍 This approach is grounded in a recent peer-reviewed paper from KAIST/Narnia Labs exploring eight application scenarios for generative AI in engineering - from 3D shape generation to simulation prediction and optimization. Instead of optimizing in a high-dimensional parameter space, they compress the design into a low-dimensional latent space - which makes real-time generative optimization possible. That’s a game-changer for anyone working on simulation-heavy or geometry-rich products. 📄 Full paper: Generative AI-driven Design Optimization (Kang, JMSTA 2025) || 🔗 DOI: 10.1007/s42791-025-00097-1 #engineering #ai #generativedesign

Most people are generating avatars or landscapes. I wanted to see if it could handle complex engineering concepts. I used it to build this technical deep dive on AI-Native CAD—specifically the shift from mesh-based approximations to true Generative NURBS and B-rep modeling. Why does this matter for Architecture? This is the bridge between "concept art" and "constructible design." If we want AI to actually help detail complex facades or steel connections, it needs to speak the language of precision geometry (NURBS), not just pixels. The results were surprisingly capable. It helped visualize: • The difference between B-rep (skeleton) and NURBS (skin). • How early AI struggled with "lossy" voxel conversions. • The new wave of "Text-to-NURBS" generation. Have you tried the new model yet? 👇 Slides below. #GenerativeAI #CAD #NanoBananaPro #DeepLearning #NURBS #DesignAutomation

Traditional Design vs Generative Design – A Shift in Engineering Thinking In the world of mechanical and aerospace engineering, design methods are evolving rapidly. The image above clearly illustrates the contrast between Traditional Design and Generative Design using an example of aircraft seat mounting brackets. 🔹 Traditional Design This approach relies on human intuition, experience, and established standards. Designers use basic geometric shapes and overengineer components to ensure safety, often leading to excess material usage and heavier parts. In the image, the traditional bracket weighs 1,672 grams, made with solid material and a blocky design to ensure strength. However, it lacks material efficiency and may contribute to increased fuel consumption in aircraft. 🔹 Generative Design This is an advanced, AI-driven design process. Engineers input goals (like weight reduction, strength requirements, material type, and load conditions), and the software generates multiple optimized design solutions. The result is often an organic, lattice-like structure that removes unnecessary material. In the image, the generatively designed bracket weighs only 766 grams — a 55% weight reduction — while still meeting performance criteria. 💡 Key Differences: Design Process: Human-driven vs AI-assisted Material Usage: Excessive vs optimized Shape: Simple, blocky vs complex, organic Efficiency: Heavier and stronger than needed vs lightweight and just as strong Generative design is not just a trend—it's a strategic shift toward sustainable, high-performance engineering. It helps industries like aerospace, automotive, and manufacturing to save weight, reduce cost, and innovate faster. This transformation is a perfect example of how technology is redefining the boundaries of what's possible in design and engineering. --- #TraditionalDesign #GenerativeDesign #MechanicalEngineering #CAD #DesignInnovation #AerospaceEngineering #LightweightDesign #TopologyOptimization #FutureOfEngineering #AutodeskFusion360 #EngineeringTransformation #ProductDesign #AIInEngineering

New research out of Department of Mechanical and Process Engineering (D-MAVT), ETH Zurich on AI Agents and CAD, offering a framework for generative geometry. "From text to design: a framework to leverage LLM agents for automated CAD generation" by Aurel Schüpbach, Raúl San Miguel Peñas, Julian Ferchow and Mirko Meboldt introduces a CAD and LLM agnostic framework to evaluate different agents and compare their performance. For their examples, the agent with vision capabilities was the most sucessful. What I find most interesting is the automated topology optimization, run with Grasshopper of course, and the tOpos plugin. Cutting edge capabilities live in Rhino! link to the paper below!

I used to spend hours drawing blueprints as an architect. Now AI is making this skill obsolete. The data behind the shift: → 30–50% faster design cycles using generative layout tools → 100+ layout permutations generated from a single brief → 20–30% improvement in space utilization → 10–25% energy savings when airflow, lighting, and thermal paths are simulated early → 40% fewer late-stage design changes thanks to digital testing What's fundamentally different? AI treats floor plans like software systems: → Pedestrian movement simulated before construction → Natural light and ventilation optimized virtually → Furniture, walls, and utilities stress-tested digitally → Cost, carbon footprint, and materials optimized in parallel This enables: → Smaller homes that feel larger → Offices designed around productivity and wellbeing → Buildings that adapt over time instead of aging poorly The biggest myth? AI replaces architects and designers. Reality: AI handles complexity and permutations. Humans focus on vision, culture, emotion, and identity. The future of architecture isn't just smart. It's generative, data-driven, and human-centric. ---- ♻️ Repost if your network needs to see this transformation ➕ Follow me (Basia Kubicka) for more AI insights 🔔 Subscribe to my newsletter for deep dives: https://air-scale.kit.com/ Opinions expressed are my own and do not represent the views, policies, or positions of my employer.

🚀 Revolutionizing Aerospace Design with Generative AI: The Future of Aircraft Efficiency 🌍✈ In the fast-paced world of aerospace engineering, every gram saved equals more fuel efficiency and less environmental impact. Here’s a game-changing example of how we’re leveraging Fusion 360’s Generative Design to reshape aircraft seat mounting brackets. 💡 The Problem: Traditional aluminum brackets, weighing in at 1,672 grams, are a significant contributor to unnecessary weight and fuel costs. 🌟 The Solution: By incorporating Generative Design, we’ve cut the weight by 54%, reducing the bracket to just 766 grams! 🔍 How it Works: • Topology Optimization: Streamlining material usage while maintaining strength and safety. • Advanced Materials: Magnesium—35% lighter than aluminum—is now a key part of the design. 🛠 Key Benefits: • Weight Reduction: The new design significantly reduces aircraft weight. • Fuel Savings: Less weight = less fuel burned = lower operational costs. • Sustainability: Lighter components contribute to reduced carbon emissions over the long term. • Cost Efficiency: Airlines can potentially save millions in fuel costs across the lifetime of their fleets. 💬 What This Means for the Future of Aerospace: This isn’t just about lighter brackets; it’s about transforming the way we think about efficiency and sustainability in aviation. ✅ Join the conversation: How do you think generative design will impact the future of aerospace engineering? Share your thoughts below! #GenerativeDesign #Fusion360 #AerospaceInnovation #SustainableDesign #FuelEfficiency #AerospaceEngineering #TechForGood #CarbonReduction #AIinEngineering #FuturisticDesign