A full span table, computed instead of estimated
A span table where every value is computed from real analysis, not estimated from a rule of thumb.
The problem
A span table is the catalogue a fabricator's customers read from. Pick a profile, a thickness, a span and a support condition, and the table tells you whether it holds. To build one honestly, you have to check every combination. That is the catch. One product line can be a thousand or more analysis models once you multiply out the profiles, thicknesses, lengths and support conditions. Almost no one runs them all, so teams take the worst case per profile and interpolate down. It is safe, but it leaves real capacity on the table, and every new profile means doing it again.
What I built
Instead of sampling, I built the analysis models computationally. Every profile, thickness, length and support condition became its own model, and the workflow solves the whole set, then generates the span tables straight from the results.
The models are not simplified stand-ins. Each one is properly built, mesh, perforations and all, so the value that lands in the table is a real analysis result rather than an estimate.
What makes this work
For every combination the workflow builds a full analysis model (mesh, perforations and all) and actually solves it. Each cell is a real result rather than an interpolation: a number in the table is backed by an analysis that was run, not a rule of thumb stretched to cover the gaps.
What changed
The catalogue reflects the real capacity of each combination instead of a cautious estimate. Every value is backed by an analysis that was actually run, which means less conservatism to design around and more of the product's real performance on the page. When a new profile comes in, it goes through the same workflow.
Notes on maintainability
The analysis method is captured once and repeated computationally, so it applies to any similarly repetitive design or analysis task.