Generative Design Optimization

Traditional surrogate-based design optimization (SBDO) is hitting a wall, especially with high-dimensional, complex designs. In this new paper, Dr. Namwoo Kang presents a next-gen framework using generative AI, integrating three key models: - Generative model (design synthesis) - Predictive model (performance estimation) - Optimization model (iterative or generative) Rather than optimizing directly in a high-dimensional design space (x), the workflow introduces a low-dimensional latent space (z) learned via generative models. ➡️ z → x → y z = latent variables x = CAD geometry y = performance (drag, stress, etc.) This means we’re no longer hand-coding design parameters or doing trial-and-error with simplified surrogate models. 🧠 Why this matters: - Parametric modeling is no longer a bottleneck - Complex shapes are learned directly from CAD - Dynamic and multimodal performance data (1D, 2D, 3D) can be used - Near real-time optimization is possible #AI #GenerativeDesign #CAE #DesignOptimization

Generative AI Is Revolutionizing the Manufacturing Design 💡 💡 Generative AI optimizes manufacturing design by swiftly generating iterations based on specified parameters, accelerating product development and yielding lightweight, efficient designs that might challenge human engineers. Here's how AI is contributing to design optimization: 👉 Generative Design: ⚪ Exploration of Design Space: Generative design algorithms explore a vast design space by considering numerous variables and constraints. This allows for the generation of design alternatives that human designers might not have considered. ⚪ Optimization of Parameters: AI algorithms optimize design parameters such as material usage, weight distribution, and structural integrity. This leads to the creation of designs that are not only efficient but often innovative in ways that may be challenging for traditional design methods. ⚪ Iterative Processes: AI facilitates rapid iteration by quickly generating and evaluating multiple design options. Designers can then focus on refining the most promising concepts, saving time and resources in the design phase. 👉 Performance Prediction: ⚪ Simulation and Analysis: AI enables advanced simulation and analysis of designs. It predicts how different design configurations will perform under various conditions, considering factors like stress, heat, and fluid dynamics. This ensures that the final design meets performance requirements. ⚪ Real-time Feedback: During the design process, AI provides real-time feedback. Designers can instantly see how modifications impact performance, enabling quick and informed decision-making. 👉 Multidisciplinary Optimization: ⚪ Integration of Multiple Disciplines: AI-driven optimization considers multiple disciplines simultaneously, such as mechanical, thermal, and fluid dynamics. This holistic approach ensures that designs are optimized across various parameters. ⚪ Trade-off Analysis: AI helps in analyzing trade-offs between conflicting design objectives. For instance, a design might need to balance factors like weight, cost, and strength. AI assists in finding the optimal compromise among these conflicting requirements. 👉 Customization and Personalization: ⚪ Tailored Solutions: AI allows for the creation of highly customized designs based on specific user requirements. This is particularly relevant in industries like automotive and aerospace, where components can be optimized for individual preferences or operational conditions. 👉 Design Speed: ⚪ Acceleration of Innovation: AI expedites the design process by automating repetitive tasks and handling complex calculations. This acceleration allows for more time to be spent on creative and innovative aspects of design. #DigitalTranformation #Innovation #Industry4 #Automation #Manufacturing ____________________________________ Follow hashtag #neerajmittra to stay connected on Digital Transformation concepts and its practical execution.

🚀 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

🔬 Engineering design synthesis is moving from manual iteration to automated, data-driven approaches. MIT researchers map out how deep generative models are enabling this shift, with important technical implications for how we develop products in the reference paper below. The paper provides a systematic analysis across: 🎯 Design Problems: • Topology optimization • Materials & microstructure design • 2D/3D shape synthesis • Multi-component product design 💾 Data Representations: • Voxels & point clouds for 3D • Images for 2D designs • Parametric specs for manufacturing • Graphs for component relationships 🧮 Model Architectures: • GANs with various conditioning approaches • VAEs for latent space exploration • RL for sequential design decisions • Integration with physics-based simulation ⚖️ Loss Functions: • Performance metrics from simulation • Manufacturability constraints • Style transfer for design aesthetics • Multi-objective optimization 📊 Key Datasets: • UIUC airfoil database • ShapeNet/ModelNet for 3D shapes • BIKED bicycle design dataset • Material microstructure collections 📝 Reference: "Deep Generative Models in Engineering Design: A Review" by Regenwetter et al. https://lnkd.in/g_mMR-8y S2 Labs #EngineeringDesign #MachineLearning #TechnicalResearch

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