AI for CAD Tools
Step-by-step guide to generative design for mechanical engineers. Learn the setup, constraints, tools, and workflow to get real results from day one.
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10 min read
Dr. Maor Farid
Maor Farid is the Co-Founder and CEO of Leo AI, the first AI platform purpose-built for mechanical engineers. He holds a PhD in Mechanical Engineering and completed postdoctoral research at MIT as a Fulbright fellow. A Forbes 30 Under 30 honoree and former AI researcher and Mechanical Engineer in an elite military intelligence, Maor leads Leo AI's mission to transform how engineering teams design better products faster.
BOTTOM LINE
Generative design is a genuinely powerful tool for mechanical engineers, but only when applied to the right problems with the right inputs. The technology is not magic. It requires careful constraint definition, realistic manufacturing specifications, and significant post-processing before results are production-ready.
Before reaching for generative design, ask whether the part you need already exists. Search your vault first. The fastest path to a production-ready result is almost always reusing or adapting a proven design rather than generating from scratch.
When you do need new, performance-optimized geometry, generative design earns its place. Set up your studies carefully, validate your results thoroughly, and plan for the cleanup time. The technology rewards methodical engineers, not those looking for shortcuts.
Generative design has been on every engineering conference agenda for the past five years, and the demos always look incredible. Organic shapes that look like they grew in nature. Weight reductions that seem impossible. Parts that perform better than anything a human would have drawn.
Then you sit down to actually use it. The software asks you to define a dozen parameters you have never thought about. The first run takes hours and produces something that looks like modern art but cannot be machined. The second run fails because your constraints were over-defined. By the third attempt, you are wondering whether you should have just modeled the part conventionally.
This guide is for mechanical engineers who want to actually use generative design, not just watch demos of it. We will walk through the real workflow, the inputs that matter, the mistakes that waste your time, and how generative design fits alongside the tools and processes you already use.
What Generative Design Actually Is (Without the Marketing Fluff)
Generative design is algorithmic exploration of a design space. You define the problem: what loads the part sees, where it connects to other components, what material you want to use, how you plan to manufacture it, and what volume it is allowed to occupy. The algorithm then generates multiple design candidates that satisfy those constraints while optimizing for objectives you specify, typically minimizing weight or maximizing stiffness.
The underlying math is usually some form of topology optimization, sometimes combined with lattice generation, shape optimization, or multi-objective evolutionary algorithms. Different software packages use different solvers, but the fundamental idea is the same: let the computer explore configurations that a human engineer would never draw by hand.
What generative design is not: it is not AI that understands your product. It does not know that the bracket it is designing sits next to a hot exhaust manifold. It does not know that your machine shop cannot hold tolerances tighter than plus or minus 0.1mm. It does not know that your assembly technician needs clearance for a socket wrench. All of that context lives in your head, and you feed it into the system through constraints. The quality of your output is directly proportional to the quality of your constraint definition.
This is the part that trips up most engineers on their first attempt. Generative design is not a magic button. It is a powerful optimization tool that requires thoughtful engineering input to produce useful results.
IN PRACTICE
Leo found a nature-inspired solution, a concept we wouldn't have thought of, that let us use standard, off-the-shelf parts.
"Leo found a nature-inspired solution, a concept we wouldn't have thought of, that let us use standard, off-the-shelf parts."
- Chen, Team Lead at ZutaCore
Setting Up Your First Generative Study, Step by Step
The workflow follows a consistent pattern regardless of which software you use. Getting these steps right is the difference between useful results and wasted compute time.
Step one: define the design space. This is the maximum volume your part is allowed to occupy. Think of it as a block of raw material. The algorithm will remove material from this block until only the structurally necessary geometry remains. Be generous with your design space. If you constrain it too tightly, you are essentially telling the algorithm what shape to produce, which defeats the purpose.
Step two: define preserved geometry. These are regions the algorithm cannot touch. Bolt holes, mating surfaces, bearing seats, seal grooves, anything that must remain exactly as specified. This is where most first-time users make their biggest mistake: they preserve too much geometry. If you preserve 80% of the part, the algorithm only has 20% to work with, and the results will be underwhelming.
Step three: define loads and boundary conditions. Apply the forces, pressures, and moments your part will experience in service. Be honest about your load cases. If you only optimize for a single load condition, the part might fail under a different one. Include thermal loads if they are significant. Include fatigue considerations if the loading is cyclic.
Step four: select materials and manufacturing constraints. This is critical. If you tell the algorithm to optimize for additive manufacturing, you will get organic lattice structures. If you specify CNC machining, the results will respect tool access and minimum feature sizes. The manufacturing constraint fundamentally changes what the algorithm produces.
Step five: run the study and evaluate results. Most tools generate multiple candidates. Do not just pick the lightest one. Evaluate each candidate against all your requirements, including the ones you did not explicitly define as constraints. Check for practical issues: can you actually fixture this part? Can you inspect it? Does it look like it belongs in your product?
The Five Mistakes That Waste the Most Time
After watching dozens of engineering teams adopt generative design, the same mistakes come up repeatedly.
Mistake one: over-constraining the design space. Engineers instinctively want to guide the algorithm toward "reasonable" shapes. They add obstacles, preserve unnecessary geometry, and restrict the volume until the algorithm has almost no freedom. Let the algorithm surprise you. That is the whole point.
Mistake two: ignoring manufacturing constraints entirely. Running a generative study without manufacturing constraints produces beautiful geometry that cannot be made. Always specify how you plan to build the part. If you are not sure, run the study with multiple manufacturing methods and compare the results.
Mistake three: optimizing for a single load case. Real parts see multiple load conditions. A bracket that is optimized purely for vertical compression might fail under lateral loading or vibration. Define all relevant load cases, even if some seem minor.
Mistake four: skipping the validation step. Generative results are optimized against the simplified model you provided. Before committing to a design, run a proper FEA validation on the cleaned-up geometry with refined mesh settings. The generative solver uses approximations for speed. Validation catches what those approximations miss.
Mistake five: treating generative design as the entire design process. It is one tool in your toolkit. The output still needs to be refined, integrated into your assembly, toleranced, documented, and reviewed. Plan for the post-processing time. It is not trivial.
When Generative Design Makes Sense (And When It Does Not)
Generative design delivers real value in specific scenarios. Knowing when to use it and when to skip it saves you from the hype cycle disappointment.
It makes sense when: weight is a primary design driver, you have well-defined loads and constraints, the part is structurally loaded and non-trivial, you have access to manufacturing methods (often additive) that can produce complex geometry, and the performance gain justifies the additional design time.
It does not make sense when: the part is simple enough to design conventionally in less time than setting up the study, the part is a commodity component that already exists as a standard off-the-shelf item, manufacturing constraints eliminate most of the design freedom anyway, or the performance requirements are easily met with conventional geometry.
Here is a reality check that rarely makes it into vendor presentations. For many parts that mechanical engineers design, the highest-value move is not optimizing from scratch. It is finding an existing, validated design that already meets your requirements. Engineering organizations accumulate thousands of parts over years of projects, and most of those parts are buried in PDM systems with search tools that barely function.
Leo AI addresses exactly this gap. Before you spend hours setting up a generative study, Leo lets you search your entire vault using natural language or even upload a rough CAD model to find geometrically similar existing parts. The parts it finds are already proven, already parametric, and already have manufacturing data attached. Leo offers integrations with leading PDM and PLM platforms, so it works with what you already have.
Fitting Generative Design Into Your Existing Workflow
The teams that succeed with generative design do not overhaul their entire process. They add it as a targeted capability within their existing workflow.
Early concept phase: use generative design to explore the design space quickly and identify structural paths you would not have considered. Treat the output as inspiration, not finished geometry.
Detailed design phase: for performance-critical parts, set up refined generative studies with accurate loads, real material properties, and specific manufacturing constraints. Budget time for the cleanup and validation steps.
Design review: present generative design candidates alongside conventional alternatives. Show the performance trade-offs. Let the team evaluate practical considerations the algorithm cannot capture: assembly access, maintenance requirements, aesthetic consistency with the rest of the product.
Documentation: when you release a generatively designed part, document the study parameters. Future engineers need to know what loads and constraints drove the geometry. Without that documentation, the organic shape looks arbitrary, and nobody will know whether it is safe to modify.
One more thing worth mentioning. The engineers getting the most value from AI tools in 2026 are not just using generative design or text-to-CAD in isolation. They are combining AI-powered search, generative optimization, and conventional modeling based on what each specific design problem requires. The goal is not adopting a single tool. It is building a workflow that uses the right approach for each situation.
FAQ
Search Before You Generate
Find existing validated parts in your vault with plain language search.
Before setting up a generative study, check whether a similar part already exists. Leo AI searches your PDM and PLM using text, geometry, and CAD-to-CAD matching. Find proven designs in minutes.
Schedule a Demo →
#1 New AI Software Globally - G2 2026
Enterprise-grade security
Trusted by world-class engineering teams
Search Before You Generate
Find existing validated parts in your vault with plain language search.
Before setting up a generative study, check whether a similar part already exists. Leo AI searches your PDM and PLM using text, geometry, and CAD-to-CAD matching. Find proven designs in minutes.
Schedule a Demo →
#1 New AI Software Globally - G2 2026
Enterprise-grade security
Trusted by world-class engineering teams