AI for CAD Tools
Stop getting unusable generative design results. This guide covers constraint setup for loads, boundaries, manufacturing, and material so your output is actually useful.
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9 min read
Michelle Ben-David
Michelle Ben-David is a mechanical engineer and Technion graduate. She served in an IDF elite technology and intelligence unit, where she developed multidisciplinary systems integrating mechanics, electronics, and advanced algorithms. Her engineering background spans robotics, medical devices, and automotive systems.
BOTTOM LINE
Generative design constraints determine whether you get a manufacturable component or an expensive screensaver. Load cases, boundary conditions, manufacturing methods, and material properties all need careful setup grounded in real engineering knowledge. The best constraint inputs come from your organization's own design history, test data, and manufacturing feedback. Leo AI makes that knowledge searchable, so every generative study starts from validated data instead of assumptions.
Every engineer who has tried generative design has had the same experience. You define your design space, set up your loads, hit solve, and the algorithm spits out an organic shape that looks like it belongs in a sci-fi movie. Then you show it to manufacturing and they laugh.
The problem is almost never the algorithm. The algorithms are doing exactly what they are told. The problem is what they are being told. Constraint setup is where generative design studies succeed or fail, and most tutorials skip over it like it is a formality. It is not. The constraints you define are the engineering judgment layer of the entire process. Get them wrong, and you get a mathematically perfect answer to the wrong question.
This guide covers the practical reality of setting up generative design constraints that produce useful, manufacturable results. Not the theoretical ideal. The actual workflow, with the common mistakes that waste days of compute time and the workarounds that experienced engineers use to get results they can actually build.
Load Case Definition: Where Most Studies Go Wrong First
The first constraint any generative design study needs is a set of load cases. And this is where the most consequential mistakes happen, because they propagate through everything downstream.
The most common error is incomplete load case definition. Engineers define the primary operating load and forget about secondary loads, transient conditions, and abuse cases. A mounting bracket might see 500N in its primary direction, but what about vibration loads during transport? Thermal expansion forces at operating temperature? The accidental 2x overload when the assembly gets dropped during installation?
Generative algorithms will ruthlessly remove material from any region that is not carrying load. If you forgot a load case, the algorithm will thin out exactly the section of material that load would have needed. The result passes your defined analysis but fails in the real world.
The second common mistake is applying loads at incorrect locations or with wrong boundary conditions. A distributed load applied as a point force produces wildly different results. A fixed boundary condition applied where there is actually a sliding contact changes the entire stress distribution. These seem like basic FEA mistakes, but they are amplified in generative design because the algorithm optimizes specifically for the load paths you define.
Best practice: before setting up any generative study, list every load case the component will see in service. Include steady-state, transient, fatigue, thermal, and abuse loads. If you are not sure about a load value, use a conservative estimate. An overbuilt result from conservative loads is far more useful than an underbuilt result from optimistic ones.
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. No custom manufacturing. No dedicated engineer.
"Leo found a nature-inspired solution - a concept we wouldn't have thought of - that let us use standard, off-the-shelf parts. No custom manufacturing. No dedicated engineer."
- Chen, Team Lead, ZutaCore
Boundary Conditions and Preserve Regions: Defining What Cannot Change
After loads, the next critical constraint set is boundary conditions and preserve regions. These define what the algorithm cannot touch and how the component interfaces with the rest of the assembly.
Preserve regions are areas where geometry must remain unchanged. Bolt holes, mounting faces, bearing bores, sealing surfaces, and interface datums all need to be locked. The algorithm will try to remove material from these regions if you do not explicitly protect them, and you will end up with an optimized shape that no longer fits in the assembly.
A mistake that catches even experienced engineers: forgetting to preserve enough material around interface features. If you preserve a bolt hole but not the surrounding boss, the algorithm might thin the wall around the hole until there is not enough material for the fastener head to seat against. Preserve regions should include not just the feature itself but the functional zone around it.
Boundary conditions define how loads enter and leave the component. Fixed supports, pinned connections, sliding contacts, and elastic foundations all produce different optimization results. Using a fully fixed boundary when the real joint allows rotation constrains the algorithm unnecessarily and produces stiffer, heavier designs than needed. Using a free boundary where there is actually a contact produces designs that flex in ways the real assembly does not allow.
The key principle: boundary conditions in a generative study should match the real assembly behavior as closely as possible. If you are not sure, run the study with multiple boundary condition assumptions and compare results. The differences will tell you which interfaces are driving the design.
Manufacturing Method Constraints: The Make-or-Break Layer
This is where generative design studies either produce something useful or produce wall art. Manufacturing constraints tell the algorithm what kinds of shapes are physically producible, and without them, you get organic lattice structures that no machine tool on earth can create.
Most commercial generative design tools now offer manufacturing method constraints for a few common processes. Here is what they typically include and what to watch for.
For CNC machining, you can usually specify the number of axes and the tool approach direction. A 3-axis constraint prevents the algorithm from creating undercuts and deep internal cavities that a 3-axis mill cannot reach. But the constraint does not account for minimum tool diameter, corner radii, or fixture accessibility. You still need to manually verify that the result is actually machinable with your available tooling.
For casting, you can define a pull direction for the mold. The algorithm avoids undercuts relative to that direction and maintains draft angles on vertical surfaces. This works reasonably well for simple parts but struggles with complex multi-piece mold configurations. The algorithm also does not account for gating, risering, or porosity in thick sections.
For additive manufacturing, the constraints typically address build orientation, overhang angles, and minimum feature size. Parts optimized for 3D printing can have internal channels and lattice structures that would be impossible with subtractive methods. But the constraints do not account for support removal accessibility, surface finish limitations, or residual stress in large builds.
For sheet metal, some tools offer bend-radius constraints and flat-pattern manufacturability checks. These are still immature in most platforms.
The critical takeaway: manufacturing constraints in generative design tools are necessary but not sufficient. They prevent the worst geometry violations but do not replace a manufacturing engineering review. Always have someone with shop floor experience look at the result before committing to it.
Material Selection and Multi-Objective Optimization
Material properties are a constraint that many engineers set once and forget. But in generative design, material choice fundamentally changes the optimal geometry.
An aluminum part optimized for minimum weight looks completely different from a steel part optimized for the same loads. The algorithm distributes material differently based on yield strength, elastic modulus, density, and fatigue properties. Running a generative study with a generic "steel" material and then changing to a specific alloy after the fact invalidates the optimization.
Multi-objective optimization adds another layer of complexity. Most real engineering problems involve trade-offs: minimize weight while maintaining stiffness. Minimize cost while meeting fatigue life. Minimize volume while ensuring thermal performance. Setting up these objectives correctly means defining relative priorities and understanding what the Pareto front looks like.
A common pitfall is setting conflicting objectives with equal priority. If you tell the algorithm to simultaneously minimize weight and maximize stiffness with equal weighting, you get a result that is mediocre at both. Better to run separate studies with different objective priorities and compare the trade-offs explicitly.
Also consider that the best material for a given application might not be the one the algorithm explores. Generative design tools optimize geometry for a given material. They do not optimize material selection. An engineer who knows that a glass-filled nylon would eliminate a secondary machining operation brings judgment that no algorithm currently captures. This kind of contextual knowledge, the kind that lives in experienced engineers' heads and in past project records, is exactly what makes or breaks a generative design study.
Getting Better Inputs: Why Organizational Knowledge Matters
The quality of any generative design study depends entirely on the quality of its inputs. And the best inputs come not from textbooks or vendor datasheets but from your organization's own design history.
What loads did similar components actually see in service? What manufacturing issues came up during production of the previous version? Which material performed best in the thermal cycling tests? What tolerance issues caused assembly problems on the last program? These answers exist somewhere in your organization. The question is whether you can find them fast enough to use them.
This is where Leo AI fits into the generative design workflow. Leo connects to your PLM and PDM systems and lets engineers search for relevant past designs, test reports, and manufacturing records using natural language. Instead of setting up a generative study based on guesswork, you can pull real load data from previous validation tests, check manufacturing feedback from the shop floor, and review how similar components were designed and why.
Leo offers integrations with leading PDM and PLM platforms including SolidWorks PDM, Autodesk Vault, PTC Windchill, Siemens Teamcenter, and Arena PLM. It reads CAD geometry natively, so you can even find geometrically similar past designs and use their validated parameters as starting points for your generative study.
The engineers who get the best results from generative design are not the ones running the fanciest algorithms. They are the ones with the best input data. Making your organization's accumulated knowledge searchable and accessible is the highest-leverage improvement you can make to any generative design workflow.
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Better Inputs, Better Designs
Pull real data from past projects instantly
Leo AI connects to your PLM and PDM vault so you can search past designs, test data, and manufacturing records in plain language. Start every design study with validated engineering knowledge.
Schedule a Demo →
#1 New AI Software Globally - G2 2026
Enterprise-grade security
Trusted by world-class engineering teams