AI for Engineering Productivity
Typed-input calculators still require re-entering dimensions and material data by hand. See how an AI layer reads that data straight from the CAD model instead.
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8 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.
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Typed-input calculators solved the formula. The next gap is the step before the formula, where dimensions and material properties get read off a model and retyped by hand, and where transcription errors quietly creep in. An AI intelligence layer that connects to CAD, PDM, and PLM systems can pull those values directly from the released model instead, and show exactly where each one came from. That does not replace an engineer's judgment about loads, standards, or safety factors. It replaces the retyping, and gives the engineer a citation to check instead of a number to trust on faith.
Ask a mechanical engineer how long a hand calculation actually takes and the honest answer is rarely the math. It is finding the right diameter, tracking down which material got assigned in the last revision, and making sure the number typed into the calculator matches the number in the model. Several posts on this site already cover AI-assisted calculations for gears, shafts, and bolted joints, and each one still starts the same way: an engineer typing values into a form.
This post looks at the other direction. Instead of typing dimensions and material properties into a calculator, an AI intelligence layer connected to the CAD assembly reads them directly off the model, runs the check, and shows its work. The math does not change. Where the numbers come from does.
The Data-Entry Step Nobody Audits
Most engineering calculation tools, including the ones covered in this overview of AI for engineering calculations, are built around a form: type in a diameter, a length, a material's yield strength, a load. The formula behind the form is usually solid, often taken straight from ASME, AGMA, or DIN. The weak point sits upstream of the formula.
Every value in that form has to come from somewhere, and it usually comes from an engineer reading it off a drawing or a CAD model and retyping it. That step introduces the same handful of errors again and again:
Pulling a dimension from a stale revision instead of the current one.
Reading the nominal value on a drawing instead of the actual model dimension.
Mixing up a material grade that looks similar to the one actually assigned.
Entering a value in the wrong unit system, especially when a spec sheet and the CAD model do not match.
None of these are calculation errors. They are transcription errors that happen before the calculation starts, and a formula applied correctly to the wrong number still gives the wrong answer.
The cost of catching one of these late is rarely small. A bolted joint sized against the wrong material grade might pass a first review and fail a torque audit months later. A shaft sized against a stale diameter might not show a problem until a supplier delivers the part and it does not fit the bearing it was meant to carry. Retyping is a small step, but it is the step where a wrong number stops looking wrong.
IN PRACTICE
Leo uses a Large Mechanical Model trained on 1M+ technical sources. It also provides citations, so we don't have to guess whether a material property or tolerance is correct. We see 96% accuracy on technical queries.
- Dorian G., AI Engineer
What It Means for an AI Layer to Read the Model
Reading a model is different from reading a drawing. A drawing shows dimensions an engineer chose to call out. A CAD model, and the PDM or PLM system that stores it, carries a lot more: the actual parametric geometry, the material assigned to each part, and how parts are constrained against each other in the assembly.
An AI intelligence layer sitting on top of that data, rather than replacing it, can pull the specific values a calculation needs: a shaft's diameter at the section under load, the material's yield strength and modulus, and which parts actually contact or constrain that shaft in the assembly. Leo offers integrations with leading PDM and PLM platforms, including SolidWorks PDM, Autodesk Vault, PTC Windchill, Siemens Teamcenter, Arena PLM, and others, so it can trace a part's current, released geometry and material instead of working from a screenshot or a typed guess.
The output is the same kind of number a typed-input calculator produces. The difference is that each value carries a source: this diameter came from this feature, on this revision, of this part.
Take a shaft with a keyway cut near a bearing seat, a common stress concentration point in shaft design. A typed-input calculator needs someone to measure the keyway width and depth off the drawing and look up the shaft material's yield strength separately. A model-driven check instead reads the keyway geometry as modeled, reads the material assigned to that part in the CAD system, and runs the same stress concentration factor against those values, flagging the exact feature if the margin comes in thin.
Where This Fits on Top of Existing PDM and PLM
This only works if the AI layer sits above the systems engineering teams already use, rather than asking them to move data into a new one. A PDM vault or a PLM system is already the source of truth for which revision is released and which material is assigned. An AI layer that reads from that system directly, instead of from an export or a re-entered copy, inherits that source of truth instead of duplicating it.
That matters most on assemblies that change often. A bracket redesigned twice this month, a shaft that picked up a fillet after a stress review, a bolt pattern that moved after a supplier substitution: in each case, a calculation built on typed-in values is only as current as the last time someone remembered to retype them. A calculation that reads the model picks up the change automatically, because it is reading the same released data everyone else is working from.
It also leaves a better trail for quality. When a calculation records which revision of a part it read, that record becomes part of the same audit trail a quality team already checks during an ECO or an NCR review, instead of a spreadsheet sitting outside the PLM system that nobody remembers to update once the part changes again.
What Still Needs an Engineer's Judgment
Reading dimensions and material properties off a model removes a data-entry step. It does not remove engineering judgment, and it should not be sold as if it does.
An engineer still has to decide what the loading actually is, since a model has no way of knowing whether a bracket will see a static load or a repeated one. An engineer still picks the applicable standard and the required safety factor, since two companies can reasonably apply different margins to the same part. And an engineer still has to sanity-check the boundary conditions, because a model can report exactly how two parts are constrained in CAD without that constraint matching how the part is actually supported once it is welded, bolted, or bonded into a larger structure.
A model-driven calculation is only as good as its citations. If it cannot show which feature, which material callout, and which revision a number came from, an engineer has no way to catch it when it is wrong.
Consider a bracket that a model-driven tool sizes correctly for a static load, because that is the load state visible in the assembly. If the bracket actually sees a repeated vibration load in service, the calculation was correct and still insufficient, because the loading assumption, not the geometry or the material, was the missing piece. That gap belongs to the engineer, not the tool.
What to Check Before You Trust the Output
Before adopting a tool that reads calculation inputs from a CAD model, it is worth checking a short list of things:
Does it show which feature or material callout each value came from, so an engineer can trace a number back to the model.
Does it flag a missing or ambiguous property instead of silently defaulting it.
Does it connect to the PDM or PLM system already in place, rather than requiring a migration or a manual export.
Does it handle mixed unit systems without quietly converting something incorrectly.
Does it cite the standard behind the formula it ran, the same way a typed-input calculator should.
A tool that gets these right tends to be the same kind of tool that can run an automated CAD design review, since both depend on reading the model correctly rather than trusting a manual re-entry. A tool that can answer yes to all five saves the retyping step without asking an engineer to trust it blindly.
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