CAD Modeling Innovations

Engineering will never be the same again. For months, everyone talked about Vibe Coding. Now Vibe Engineering is becoming real. Last weekend, I decided to test something. Instead of opening CAD and clicking through sketches, I built a workflow where I simply described a component and let AI construct it for me. No manual modeling. No GUI-driven feature creation. Just a prompt. I wrote the technical specifications of a CAD part. Seconds later, the geometry appeared in Onshape by PTC, fully parametrized and built step by step. This wasn’t a demo from a big tech lab. It was my weekend project. And it made one thing very clear: We’re shifting from GUI-driven construction to prompt-driven construction. AI is becoming the mediator between engineer and CAD system. Core thesis: The future of CAD is not clicking features, it’s describing intent. Here’s the workflow I built: 1. I write a structured prompt with the technical specifications of the part. 2. Claude Code (embedded in an IDE, in my case Google's Antigravity) calls Claude's Opus 4.6. 3. Opus 4.6 generates parametrized Python code that constructs the part sequentially. 4. Claude Code executes that Python code. 5. The code activates an MCP server and sends REST API calls for every construction step. 6. Onshape by PTC builds the geometry automatically, feature by feature. Intent → code → API → geometry. The consequences are hard to ignore: • Massive acceleration of construction tasks • Near-instant design iterations • Lower barrier to entry for CAD tools • Engineers shift from “modeling operators” to “design architects” Yes, you still need engineering expertise. You still need to understand tolerances, constraints, manufacturability. But execution is no longer limited by tool fluency. The bottleneck is moving. From mouse skills to clarity of thought. From feature clicking to technical articulation. CAD is becoming democratized. If you can clearly formulate what should exist and give technically clean instructions, you can construct. Vibe Engineering isn’t hype. It’s already possible. The question is: Are we ready to train engineers for a world where describing intent matters more than mastering the interface? Vlad Larichev | Timmo Sturm | Dr. Pascalis Trentsios | Rick Bouter | Holger Wienecke

⚡️Big step toward software defined product development: this new AI model turns raw 3D point clouds into editable code for fully programmable 3D parts 😳 A new AI model MeshCoder converts raw 3D point clouds - the kind captured by LiDAR or photogrammetry - into editable, parametric code. Not mesh files. Not STLs. Executable CAD instructions that AI systems can read, modify, and regenerate. 🗄️ 𝗜𝘁 𝗽𝘂𝘀𝗵𝗲𝘀 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗳𝘂𝗿𝘁𝗵𝗲𝗿 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝘄𝗼𝗿𝗹𝗱 𝗮𝘀 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, where products 𝗮𝗿𝗲 𝗲𝘅𝗽𝗿𝗲𝘀𝘀𝗲𝗱 𝗶𝗻 𝗰𝗼𝗱𝗲, 𝘃𝗲𝗿𝘀𝗶𝗼𝗻𝗲𝗱, 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝗶𝘇𝗲𝗱, 𝗮𝗻𝗱 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝗯𝘆 𝗔𝗜 systems at a structural level. When a model can convert a physical shape into code, it becomes possible for AI to reason about the part, modify it, and generate new versions without manual CAD work. This sits perfectly in the broader shift we're already seeing: 🔹 Geometry becomes readable and editable code, not opaque mesh files 🔹 Part variations can be generated automatically for simulation and testing 🔹 AI systems can analyze and transform components programmatically 🔹 Product development becomes more traceable, reproducible, and automatable 🔹 Designers, engineers, and AI can operate on the same representation: code There is also an amazing arXiv paper in the comments, which shows how this conversion works at the technical level. AI is getting closer to understanding physical products through their code representation, and this is an early glimpse of how future design loops might run end to end in software 👏 Dr. Dirk Alexander Molitor Christian Heining Daniel Spiess

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

A New Frontier for 3D Modeling: From Painful CAD to Limitless Possibilities For years, the most painful aspect of 3D modeling in manufacturing and design has been the creation and maintenance of the models themselves. Generating a CAD layout from scratch, updating it with every change, and ensuring accuracy across teams - these steps have always been bottlenecks, slowing down innovation and optimization. But what if that pain point is about to disappear? With the advent of generative models like SAM 3D by Meta, we’re entering a new era where 3D models can be created and continuously updated directly from images. No more manual CAD redraws for every tweak. Imagine a world where your digital twin is always in sync with the real world - every optimization, every flow change in manufacturing, instantly reflected in your 3D environment. This unlocks a whole new dimension for manufacturing optimization. Visualizing changes in real time, running simulations, and collaborating in platforms like NVIDIA Omniverse becomes seamless. The applications are truly limitless—from rapid prototyping to predictive maintenance, from immersive training to next-gen AR/VR experiences. The inspiration here is clear: just as SAM 2D segmentation revolutionized how we extract meaning from images, these new 3D models are set to transform how we interact with the physical world. I’ve seen firsthand the power of segmentation in projects close to my heart—like brain segmentation for medical imaging (by Sovesh Mohapatra - disclosure - he is my younger brother :D ). The leap from 2D to 3D, powered by models like SAM 3D, is nothing short of extraordinary. I can only imagine where this is headed next. The future of 3D modeling isn’t just about making things easier—it’s about making the impossible, possible. #3DModeling #DigitalTwin #Manufacturing #AI #NVIDIAOmniverse #SAM3D #Innovation #ContinuousImprovement

Why Implicit Modeling is Reshaping Engineering Design In traditional CAD, boundary representation (B-Rep) modeling has long been the standard. It works - but it struggles when things get complex. Think blends, lattices, generative structures, or multi-physics-driven shapes. That's where implicit modeling takes off. Here’s why it matters: ↩️ Design Freedom Implicit models define geometry with mathematical fields - not surfaces. That means you can blend, merge, and deform shapes without worrying about broken topology or patching tiny gaps. Want to transition from a solid to a lattice seamlessly? It just works. ↩️ Robust Parametric Changes Unlike B-Rep, implicit models aren’t brittle when you push design changes. Parametric updates don’t cause topological failures or require manual repair. This enables faster iteration and encourages creative, non-linear design thinking. ↩️ Performance & Scalability Implicit engines are optimized for modern computers, including GPU acceleration and parallelization. That makes operations like filleting, thickening, or field-based transformations orders of magnitude faster, especially in high-resolution or multi-material parts. ↩️ Engineering-Grade Workflows This isn’t just eye candy. Implicit modeling supports downstream use in simulation, AM, and CFD, where mesh quality and model robustness are critical. It reduces prep time and increases trust in automated pipelines. In short: Implicit modeling enables engineers to focus on intent, not geometry cleanup. The model you see below is built on pure implicit modeling. It reflects more free-form shape changes (shape of the blades) as well as relation-based standard feature modeling (number of holes changing with the diameter). All modeled by using mathematical fields, no need to sketch section by section. Curious how this is changing your industry? Or want to share your experience transitioning from B-Rep to implicit? Happy to see. Are you interested to learn more about implicit modeling? Let’s talk. Or follow me and enjoy the upcoming posts in the next few weeks with more background: 1/4 What is implicit modeling 2/4 Design freedom with implicit modeling 3/4 Robustness of implicit modeling 4/4 Speed of implicit modeling  Model credits to Tristan Antonsen. Paper reference for the model: Fricke, K., et al. (2021). Geometry Model and Approach for Future Blisk LCA. IOP Conf. Ser.: Mater. Sci. Eng., 1024, 012067. Link in the comments

The future of engineering is generative, intelligent, and deeply domain-aware. At #Siemens, we're building a new kind of Foundation Model—not just trained on internet-scale data, but grounded in the physics, geometry, and logic of the industrial world. While models like GPT-4 have reshaped content creation and conversation, our Foundation Model aims to transform how we design, simulate, and automate everything from jet engines to energy grids. Trained on rich engineering data—from CAD, CAE, DM and automation logic—this model doesn't just predict words. It understands parts, tolerances, constraints, workflows, and real-world behavior. This isn’t about replacing engineers. It’s about augmenting human creativity with AI that speaks the language of design, manufacturing, and systems. Integrated into NX, Teamcenter, Industrial Copilot, and Digital Manufacturing platforms, our Foundation Model will empower engineers to: - Generate complex geometry from intent - Predict performance without full simulation - Translate ideas into production-ready models—in minutes This is what domain-specific AI at industrial scale looks like. https://lnkd.in/gq47QH7S #IndustrialAI #SiemensXcelerator #IndustrialFoundationModel #GenerativeEngineering #AIinDesign

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!