AI for CAD Tools
How to set up manufacturing method constraints in generative design so the output is actually machinable. CNC, casting, sheet metal, and additive covered.
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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
Manufacturing constraints in generative design tools prevent the worst geometry violations but do not replace manufacturing engineering judgment. CNC, casting, sheet metal, and AM constraints each have significant gaps that require manual review. The best generative design results come from teams that feed real manufacturing data into their constraint setup, not textbook defaults. Leo AI makes your organization's manufacturing knowledge searchable so every study starts with validated production data.
There is a recurring scene in every engineering department that has tried generative design. The algorithm runs overnight, produces an elegant, organic shape that meets every structural requirement, and the design engineer presents it in the next review meeting. Then the manufacturing engineer looks at it, points at three different features, and says: "How exactly are we supposed to make that?"
Manufacturing constraints are the most underappreciated layer of any generative design setup. Get them wrong, or leave them out entirely, and you produce geometry that lives exclusively on screen. Get them right, and generative design becomes a tool that actually delivers parts to a machine shop, a foundry, or a build plate.
This guide is specifically about the manufacturing side of constraint setup. Not loads. Not boundary conditions. Just the practical details of telling the algorithm what your shop can and cannot produce, and the gaps in current tools that you need to cover with engineering judgment.
CNC Machining Constraints: What the Tools Get Right and What They Miss
CNC machining is the most common manufacturing method for precision metal parts, and generative design tools have made the most progress here in terms of constraint support.
The basic constraint is tool approach direction. You specify which directions a cutting tool can access the part, and the algorithm avoids creating geometry that requires tool access from blocked directions. For 3-axis machining, this typically means specifying a single primary axis plus a flip orientation. The algorithm keeps all features accessible from one side or the other.
This works reasonably well for simple parts. Pockets, holes, slots, and boss features that a 3-axis mill can reach are maintained in the optimized result. The topology stays within what a standard milling setup can handle.
Where it falls short is in the details that determine whether a programmer can actually write the G-code. Minimum internal corner radii are often not constrained, so the algorithm produces sharp internal corners that would require an infinitely small end mill. Minimum wall thickness might be set globally but not adjusted for tall, thin walls that will chatter during machining. Deep narrow pockets that require long-reach tooling with reduced rigidity are not flagged.
Fixture accessibility is completely absent from current tools. The algorithm might produce a part that is theoretically machinable but has no way to clamp it in a vise or fixture it on a plate. The optimized shape might even eliminate flat reference surfaces that you would normally use for work holding.
For 5-axis machining, the constraints are more permissive, allowing undercuts and complex curvatures that 3-axis cannot reach. But 5-axis time is expensive, and the algorithm does not distinguish between geometry that needs full 5-axis simultaneous capability and geometry that can be cut with simpler 3+2 positioning. This difference has significant cost implications that the optimization ignores.
Practical approach: run the generative study with CNC constraints enabled, then have your machinist or CNC programmer review the result before committing to detailed design. They will spot the fixturing, tooling, and programming issues that the algorithm misses in about ten minutes.
IN PRACTICE
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"The search in Teamcenter has always been a weak point for us. If you don't know the exact part number or file name, you're basically not finding it. Leo changed that."
- Verified User, Defense and Space Enterprise
Casting Constraints: Mold Pull, Draft, and the Reality of Foundry Work
Casting constraints in generative design tools are designed to ensure the optimized geometry can be extracted from a mold. The primary constraint is pull direction, which defines the axis along which the mold halves separate. The algorithm maintains draft angles on surfaces parallel to the pull direction and avoids undercuts that would trap the part in the mold.
For simple two-part mold configurations, this works. The result is castable in the basic sense that you could physically remove it from a mold. But foundry work involves much more than pull direction and draft.
Gating and risering are not modeled. Where molten metal enters the mold, where risers compensate for shrinkage, and how the fill pattern affects porosity are all critical to getting a sound casting. The generative algorithm might place thick sections far from where a gate can practically reach, creating shrinkage porosity that only shows up in X-ray inspection after the first batch is poured.
Minimum section thickness varies with casting process and alloy. Sand casting, investment casting, die casting, and permanent mold casting each have different minimum wall capabilities. The algorithm typically uses a single global minimum thickness that may not match your actual process.
Core complexity is ignored. Internal passages and cavities that require sand cores add tooling cost and manufacturing risk. The algorithm might produce internal channels that require multi-piece cores with thin, fragile cross-sections that break during pouring.
Parting line location affects surface finish, flash, and dimensional accuracy. Where the mold halves meet determines which surfaces can be held to tight tolerances and which will have witness lines. The algorithm does not consider parting line aesthetics or functional impacts.
If you are designing a cast component with generative design, treat the algorithm output as a starting geometry. You will need a foundry engineer or pattern maker to review it for producibility before finalizing the design.
Sheet Metal Constraints: Bends, Flat Patterns, and Forming Limits
Sheet metal generative design is the least mature constraint category in current tools, which is unfortunate because sheet metal is one of the most common manufacturing processes in mechanical engineering.
The fundamental challenge is that sheet metal parts start as flat stock and are formed into 3D shapes through bending, stamping, drawing, and other operations. The geometry is inherently constrained by what you can do to a flat sheet. Minimum bend radius depends on material and thickness. Bend proximity to other features is limited by die access. Total draw depth relative to blank diameter determines whether you need single or multi-stage progressive tooling.
Most generative design tools do not handle sheet metal as a native manufacturing method. Some offer a rudimentary constraint that keeps wall thickness constant and avoids features that cannot be formed from flat stock. But the detail level is far below what a sheet metal design engineer needs.
Bend relief cutouts, hem features, lance and form tabs, dimples, and louvers are all standard sheet metal features that generative algorithms do not create. The output might be a constant-thickness structure that is generally formable, but it bears little resemblance to a production sheet metal part.
The flat pattern is the ultimate test. If the optimized 3D shape cannot be unfolded into a flat blank that can be cut from standard sheet stock, it is not a sheet metal part regardless of what the algorithm says. Current tools do not verify flat-pattern feasibility as part of the optimization loop.
For sheet metal applications, generative design is best used as a conceptual tool. Let the algorithm suggest where material should go, then translate that concept into proper sheet metal features using your CAD system's sheet metal tools. Do not expect the algorithm to produce a ready-to-fabricate flat pattern.
Additive Manufacturing Constraints: Build Orientation, Supports, and Post-Processing
Additive manufacturing is where generative design has the most geometric freedom, and paradoxically, where constraint setup requires the most care.
Build orientation is the primary constraint. It determines which direction is "up" during the print, and therefore which surfaces need support structures. Most tools let you specify the build direction and then constrain overhang angles to minimize support requirements. Typical overhang limits are 45 degrees from horizontal for metal powder bed fusion.
But support minimization is not the same as support elimination. Most complex optimized geometries will still need some support structures, and those supports need to be removable. Internal channels, enclosed cavities, and deep narrow features can trap support material permanently. The algorithm should avoid these geometries, but current constraints are not always fine-grained enough to prevent them.
Minimum feature size is another critical constraint. Laser spot size, layer thickness, and material properties determine the smallest features that can be reliably built. Thin walls, small holes, and fine lattice struts below the minimum feature size will either not resolve or will distort during the build. Set the minimum feature size constraint based on your specific machine and material combination, not the tool's default value.
Post-processing requirements are completely outside the optimization loop. Almost every metal AM part needs post-processing: stress relief heat treatment, support removal, surface finishing, HIP for porosity reduction, and precision machining of critical features. The geometry that comes out of the generative study needs to accommodate these steps. Leave stock on machined surfaces. Ensure access for support removal tools. Maintain datum features for fixturing during post-machining.
Residual stress and distortion during the build are not modeled by generative tools but significantly affect large or complex parts. A design that looks optimal in the FEA but warps off the build plate during printing is not useful. Separate build simulation tools can predict distortion, but they are not integrated into the generative optimization loop.
Bridging the Knowledge Gap: Past Manufacturing Data Matters
The recurring theme across every manufacturing method is the same: generative design tools handle the basic geometric constraints but miss the practical manufacturing knowledge that determines whether a part actually gets produced successfully.
That knowledge exists in your organization. The machinist who knows that the 3-axis Haas tops out at 400mm in Z. The foundry engineer who documented porosity issues in thick-to-thin transitions on the last aluminum casting program. The AM technician who learned that overhangs above 35 degrees (not the textbook 45) work better on your specific machine with your specific powder.
This is institutional manufacturing knowledge, and it is incredibly valuable for setting up generative design constraints correctly. The problem is that it usually lives in people's heads, in scattered email threads, or buried in manufacturing deviation reports that nobody can find.
Leo AI helps engineers access this knowledge by connecting to PLM and PDM systems and making past manufacturing data searchable in plain language. Need to know what issues came up when a similar part was cast at your foundry? Ask Leo. Want to find the build parameters that worked for a geometrically similar AM part? Leo reads CAD geometry natively and can find similar components across your entire design history.
Leo offers integrations with leading PDM and PLM platforms including SolidWorks PDM, Autodesk Vault, PTC Windchill, Siemens Teamcenter, and Arena PLM. All data stays in your secure environment with SOC-2 certification and GDPR compliance. No customer data is used for AI training.
The engineers who get the best manufacturing results from generative design are the ones who feed the algorithm real manufacturing knowledge, not textbook defaults. Making that knowledge findable is the single highest-impact investment you can make in your generative design capability.
FAQ
Real Manufacturing Data, Fast
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Leo AI connects to your PLM vault so engineers can find manufacturing deviation reports, build parameters, and lessons learned from past programs. Set up better constraints with real shop floor data.
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Real Manufacturing Data, Fast
Search past production records in plain language
Leo AI connects to your PLM vault so engineers can find manufacturing deviation reports, build parameters, and lessons learned from past programs. Set up better constraints with real shop floor data.
Schedule a Demo →
#1 New AI Software Globally - G2 2026
Enterprise-grade security
Trusted by world-class engineering teams