Mechanical Engineering CAD Models

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

🚀 In this tutorial, I demonstrate a powerful new feature in NX CAD that significantly improves surface creation and quality. The "Split Output Along Boundary Curves" toggle, introduced in updates 2412 and expanded in 2506, addresses long-standing issues with surface continuity across boundaries. 🎯 What You'll Learn: How to use the new Split Output Along Boundary Curves feature Why this toggle should be your default setting for most surface operations How it eliminates tangent breaks and discontinuities automatically ✨ When to use Parameter vs Arc Length alignment options Applications across multiple surfacing tools (Through Curves, Through Curve Mesh, Studio Surface, Ruled, and Swept features) 🏆 Key Benefits Covered: Eliminates the need for manual surface rebuilding and patching Automatically maintains proper continuity across surface boundaries Reduces extra work traditionally required for smooth surface transitions Creates cleaner, simpler surfaces with better mathematical properties 🔧 This feature is available in Through Curves, Through Curve Mesh, Studio Surface, Ruled, and Swept operations, making it a comprehensive improvement to NX's surfacing capabilities. 🎯 Perfect for intermediate to advanced NX users looking to improve their surfacing workflow and create higher-quality surfaces with less manual intervention. 📌 Timestamps and detailed breakdown available in comments below. 🔔 Don't forget to subscribe for more NX CAD tips and advanced surfacing techniques! https://lnkd.in/gjtDqemx 🏷��� HASHTAGS: #NXcad #CAD #CADTutorial #3DDesign #SurfaceModeling #NXTutorial #CADTips #Engineering #Design #Tutorial #SurfaceContinuity #ParametricDesign #CADTraining #NXDesign #3DModeling #CADSoftware #ProductDesign #TechnicalDesign #CADTricks #ContinuousImprovement #SurfaceDesign #CADDesign #3DModelingTips #NXCADTutorial #SurfaceQuality #CADInnovation

Today, Backflip AI is unveiling a new Foundation model that can build precise, engineering parts in existing 3D design packages. This breakthrough will dramatically accelerate the pace of hardware development and drive down the cost of manufacturing. Our new AI model solves the long-standing pain of converting a 3D scan into a parametric CAD model. In one click. We finally did it. 3D scanners map the surface of an object with incredible precision, quickly generating millions of data points, but they produce micro surface textures that can’t be manufactured with traditional tools. Our technology automatically converts these intricate surfaces into clean geometries designed for existing 3D CAD and manufacturing software. The first model will be available to early users in a month. You can access it online, or through a SOLIDWORKS plugin. After the AI generates a 3D model, it will drive Solidworks to create a native file with a full feature tree you can edit. Here's a cool article from Michael Alba at engineering.com (link in the comments). There are two target users for this new AI model. The obvious one is existing CAD designers who want to save hours of their life by automatically converting a scan to CAD. We're so excited to be done doing that by hand. The second set of users is much bigger. For a given automotive factory, there may be 1-2 CAD engineers, and 2,000-5,000 brilliant, mechanically savvy technicians assembling the cars / keeping the lines running. But many don't know CAD. Our new AI model will flatten the learning curve and help them get all the parts around them into parametric CAD. In the near future, everyone will be able to create the world around them.

I’ve reviewed CAD that looked flawless: ✔ Fully constrained ✔ No rebuild errors ✔ Everything aligned and clean But dig deeper—and you find: ▸ Bolts that can’t be reached ▸ Tolerances that stack the wrong way ▸ Interferences that don’t show until it’s too late ▸ No plan for fixturing, welding, or inspection That’s the illusion of clean CAD: When a model looks “done” but was never engineered to work. CAD is visual. But manufacturing is physical. If your design hasn’t been pressure-tested in the real world, then all that perfection is just a false positive. Don’t let visual clarity hide mechanical risk. Ever caught a "clean" model that nearly became a costly mistake? #engineering #cad #designreview #productdevelopment #solidworks #solidedge #projektdesign #dfm #mechanicalengineering #manufacturing

I'm building a Yes/No checklist for reviewing parting line quality at the virtual stage using some guides what I could find online. What critical questions do you use in CAD or CAE reviews to ensure high perceived quality before tooling is cut? [A] DESIGN INTENT ☐ Is the parting line located in a low-visibility or subordinate zone? ☐ Has the parting line been reviewed against customer PQ zones (e.g. touch points, line of sight)? ☐ Has the parting line been symbolised using ISO 10135 for clarity in functional vs subordinate zones? ☐ Are parting line tolerances and maximum acceptable offset defined in CAD drawings? [B] TOOL DESIGN & MACHINING STRATEGY (DIN 16742 Focus) ☐ Are centering and clamping features defined to prevent tool offset? ☐ Are mechanical tolerances of mating components within spec to avoid visible mismatch? ☐ Are guide surfaces created in CAD near parting edges to control CAM strategy? ☐ Does the CAM strategy use side-of-tool cuts along the parting line (not the tip)? ☐ Are toolpaths extended in X, Y, and Z beyond the parting line to prevent roll-over? ☐ Was a toolpath simulation reviewed for mismatch, flash, or stepped edges? [C] DRAFT ANGLE & DEMOULDING VALIDATION ☐ Is the draft angle in the parting line region ≥ recommended draft° for textured surfaces? ☐ Has the draft direction been aligned with parting plane to avoid drag marks? ☐ Was the draft orientation validated in Moldflow or virtual DFM tool? [D] TEXTURE STRATEGY ☐ Is the parting line placed where texture or graining can mask it (e.g., grain wrap-around)? ☐ Has a surface finish specification (e.g., VDI, SPI, or Etching No.) been defined near parting? ☐ Is the graining split strategy discussed with the toolmaker to avoid visible boundary lines? ☐ Has the texture flow been checked for uniformity and avoidance of sudden transitions? Please, Let me know what I’ve missed 👇

FEA: Simplify CAD, Keep the Physics Rules, checks, and traps → see the carousel. Quick carousel on reminders of 𝘄𝗵𝗲𝗻/𝘄𝗵𝗮𝘁 𝘁𝗼 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝗻 𝗖𝗔𝗗 𝗯𝗲𝗳𝗼𝗿𝗲 𝗺𝗲𝘀𝗵𝗶𝗻𝗴—and how to 𝗽𝗿𝗼𝘃𝗲 you didn’t lose accuracy. 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻𝘀𝗶𝗱𝗲 • 📐 𝗦𝗰𝗮𝗹𝗲 𝗿𝘂𝗹𝗲𝘀: s/t < 0.05, s/h_e < 0.3 (unless it’s a hot spot). • 🔍 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻: compare δ, E, and local σ (von Mises)  across mesh levels, verify if the change is less than the target goal. • ⚠️ 𝗧𝗿𝗮𝗽𝘀: broken load paths, removed notches/fillets, “bonded” where contact matters. • 🔧 𝗠𝗶𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻𝘀: equivalent beams/springs, K_t corrections, realistic contact & friction. ��𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 1. Over-simplify → broken load paths.  2. Under-simplify → wasted DOFs.  3. Aim for 𝗽𝗵𝘆𝘀𝗶𝗰𝘀-𝗳𝗮𝗶𝘁𝗵𝗳𝘂𝗹 models that converge on the QoIs you’ll use for design. Don't forget to check out the checklist at the end (last page). 𝗣.𝗦. Do you agree with these scale metrics? Share a before/after (geometry or mesh), with idealized CAD geometry for FEA, note element count, and how the stresses/Qols changed. #FEAMindset 

⚡ Free n8n Workflow for CAD-BIM 𝗔𝗨𝗧𝗢𝗠𝗔𝗧𝗘𝗗 𝗘𝗟𝗘𝗠𝗘𝗡𝗧 𝗖𝗟𝗔𝗦𝗦𝗜𝗙𝗜𝗖𝗔𝗧𝗜𝗢𝗡 𝗪𝗜𝗧𝗛 𝗟𝗟𝗠 & 𝗥𝗔𝗚 (works with Revit/IFC/DWG/DGN) Today, BIM and CAD specialists still spend significant time manually classifying elements, checking attributes, and aligning models with internal standards. This process is becoming more and more like what we did in the past: ▪️ photos were sorted into albums by hand ▪️ forum posts required careful selection of the “right category” ▪️ finding a video meant endless navigation through folders ML and AI have already freed us from that routine — and the same shift is now happening with CAD-BIM projects. LLMs and RAG will gradually take over the tasks of classification and data validation, while specialists evolve from “operators” to process architects. When trained with RAG on your BIM Execution Plan (BEP) or internal classification rules in XLSX or PDF format, the workflow acts as an intelligent auditor that can: ▪️ detect classification errors ▪️ highlight deviations from corporate naming standards ▪️ suggest corrections based on accumulated knowledge 𝗛𝗼𝘄 𝘁𝗵𝗲 𝗻𝟴𝗻 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝘄𝗼𝗿𝗸𝘀 1️⃣ Conversion — Revit / IFC / DWG / DGN → Open Database 2️⃣ Extraction — clean headers & select grouping parameter 3️⃣ Grouping — count elements & volumes 4️⃣ LLM + RAG — automatic classification by codes & standards 5️⃣ Reporting — Excel & HTML with summaries and charts Processing 1,000 elements with GPT-4.1-mini costs only a few cents. For maximum accuracy, I recommend the most effective models - Opus 4, Grok 4, Gemini 2.5 and ChatGPT5. 𝗣𝗮𝗿𝗮𝗱𝗶𝗴𝗺 𝘀𝗵𝗶𝗳𝘁 We’re moving from manual quality control to probability management. BIM professionals will increasingly act as conductors: defining rules, training models on corporate data, and making decisions in edge cases. This isn’t about replacing expertise - it’s about amplifying it: less routine, more analysis and decision-making. Explore the workflow: 🔗 𝗚𝗶𝘁𝗛𝘂𝗯: https://lnkd.in/eJyaySSR 📄 File: n8n_5_CAD_BIM_Automatic_Classification_with_LLM_and_RAG.json Examples of project classifications according to different classifiers will be posted later in our telegram group (links in messages). Distributed small solutions, simple 𝗻𝟴𝗻 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗰𝗿𝗲𝗮𝘁𝗲 "𝗮𝗻𝘁𝗶𝗳𝗿𝗮𝗴𝗶𝗹𝗶𝘁𝘆" for business - which is of course extremely boring in calm times, but extremely vital during crises. 𝗧𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝗰𝗮𝗻𝗻𝗼𝘁 𝗯𝗲 𝗮𝗻𝘁𝗶𝗳𝗿𝗮𝗴𝗶𝗹𝗲 𝗱𝘂𝗿𝗶𝗻𝗴 𝗽𝗲𝗿𝗶𝗼𝗱𝘀 𝗼𝗳 𝘁𝘂𝗿𝗯𝘂𝗹𝗲𝗻𝗰𝗲 - 𝗹𝗲𝗮𝘃𝗲 𝘁𝗵𝗲 𝗺𝗮𝗿𝗸𝗲𝘁. 👉 If you need help testing n8n solutions with RAG and LLM on your data or adapting the workflow to real project tasks, contact us. ♻️ Share this with colleagues who still believe that manual checking and classification is “normal work.” In reality, these tasks can already be automated with LLMs and n8n workflows.

A chief engineer reached out to us today & this was top of mind for new capabilities he needs: "Modeling families of air vehicles to varying missions, Automation of performance analysis, trade studies, multi-disciplinary optimizations including cost, Design automation direct from requirements." Here's what's interesting about that list: each item forces a tradeoff: do you go low-fidelity and fast, or high-fidelity and slow. Neither option is good. You can definitely go fast drawing up quick planforms or tubes with wings, but will the design close when trying to integrate all of the real stuff? Usually you need a high-fidelity CAD model to know this, but by the time it's modeled up and nothing fits, it's too late. Higher-fidelity parametric models break when flexed, even undergoing small changes like changing the leading edge angle I've seen cause errors. Faster speed only reinforces the Lock-In Trap. Teams freeze architecture early because exploring alternatives feels too slow, and end up over many month- long cycles trying to close out the design, possibly one that might not close. Next week, he'll sit with an nTop engineer to go through a workflow that shows exactly what he's asking for: 1) UAV family modeling: Fully parametric models that never break when you change parameters. Build once, scale across your entire family. 2) Performance analysis automation: Embedded analysis (LBM, AVL/XFOIL, DATCOM, SUAVE integration) gives instant performance feedback as you modify geometry. No export workflows. 3) Trade studies & MDO: Generate hundreds of variants automatically, all simulation-ready. Zero geometry failures in optimization loops. 4) Requirements to design: Encode mission requirements directly into parametric logic that drives geometry generation. The programs that win will be the ones that stop accepting the speed vs fidelity tradeoff. If you're dealing with the same constraints, DM me.

Autodesk putting Anthropic AI Claude “inside” their Fusion Product Announcement It is another signal about where VDC, BIM, Revit, and Digital Twins may be headed in the near future. High-level Overview: Company Revenue __Autodesk: ~$6 billion annual revenue. Fiscal 2026 guidance $6.9B to $7.0B. Public company. __Anthropic: ~$5B to $9B annualized run-rate revenue, recent claims of $30B annualized 2026 run rate. Private company. The obvious story is prompt-to-geometry modeling. Type an instruction. Get model(s). Most buildings are not born as clean models. They arrive as PDFs, scans, redlines, hand sketches, legacy drawings, and incomplete field information. That messy information is where project risk resides. When AI can help convert those documents into structured model data, the workflow changes: Designer markup → AI-assisted model update → Technician review → Estimator validation → Model-linked cost, Carbon, LEED, Schedule, Operations intelligence, and living Digital Twins. That is the bridge from BIM to digital twins. Not an easier or prettier model. A more structured decision system. For Revit, this is the real test. Can AI help turn fragmented project information into reliable objects, assemblies, quantities, spaces, systems, and relationships? Can it preserve source traceability? Can it tell us what is known, what is inferred, and what still needs human review? That matters for estimators. A model quantity without provenance is not intelligence. It is a risk with a clean interface. The firms that benefit will not be the ones with the cleverest prompts. They will be the ones with the strongest standards. Object standards. Quality gates. Model confidence rules. Estimating handoff protocols. Feedback loops from field performance back into preconstruction workflow. Autodesk can provide the foundation. Anthropic can provide the AI data layer. But expert practice and training must provide the rules of trust. That is the strategic point. The future of BIM is not just automation. It is governed automation. The future of Revit is not just modeling. It is decision infrastructure. The future of digital twins is not just visualization. It is traceable, validated, structured information that owners, designers, estimators, and builders can act on. An automated model is not the goal. A more trustworthy decision structure is. And it has begun… #Autodesk #Anthropic #ClaudeAI #AutodeskFusion #Revit #BIM #VDC #DigitalTwins #Precon #Preconstruction #ConstructionTechnology #AECIndustry #Estimating #ConstructionEstimating #ArtificialIntelligence #BuiltEnvironment #ConstructionInnovation #ModelBasedEstimating #DataGovernance #SourceTraceability #DigitalConstruction