Where AI Connects to Parametric Workflows

Configuration Validation

A parametric model with 15 input parameters can theoretically generate thousands of configurations. Not all of them are valid. Some parameter combinations produce geometry that violates manufacturing constraints, some produce assemblies that physically interfere, and some fall outside the range of validated material properties or standard component sizes.

AI tools that read CAD geometry can validate a specific configuration against manufacturing rules, standard component availability, and internal design guidelines without requiring the engineer to manually check each combination. Leo's design inspection runs on a configured instance of a parametric model the same way it runs on a fixed-dimension part: read the geometry, check it against the applicable rules, cite the violations.

This is particularly useful for engineer-to-order products where sales configures a product to customer specifications and engineering needs to validate that the configured instance is manufacturable before releasing the order.

Finding Existing Parametric Families

When starting a new product variant, the first question should be whether a parametric family for that product type already exists in the PDM. Not just whether a similar part exists, but whether a parametric master model exists that can be configured to cover the new variant without creating a new model from scratch.

Leo's geometric similarity search helps identify whether an existing parametric family covers the requirement. An engineer selects the key geometry of the new variant and Leo returns existing parts and families from the PDM with dimensional comparison data. If a 92% geometrically similar family already exists and the new variant falls within its parameter range, extending that family is faster and more maintainable than creating a new model.

Accessing the Design Rationale for Parameter Limits

Parametric models typically have parameter ranges, minimum and maximum values outside which the model either breaks or produces invalid geometry. The rationale for those limits is often undocumented and lives in the head of the engineer who built the master model.

With Leo AI indexed to the organization's PDM and document archive, an engineer can ask why the wall thickness parameter has a minimum of 4mm on a specific product family and get back the original stress analysis that set that limit, the applicable standard that was referenced, and the design note from the engineer who made the decision. That information does not disappear when the engineer who built the model leaves the organization.

This is the tribal knowledge problem applied specifically to parametric modeling: the model exists, but the constraints embedded in it are often opaque to anyone who was not there when it was built.