Learning from how material moves

Self-learning controls may be getting more attention today, but they are not new to Fastweigh Silo Loadout. When we released the module in 2024, auto-learning was already part of how Fastweigh helped asphalt producers manage silo drops.

Fastweigh uses previous drop results to make the next drop more informed.

Accounting for freefall

Material does not stop moving the moment a silo gate receives a close command. Some asphalt is already in motion and continues falling into the truck. This is known as freefall.

Fastweigh compares completed drops with their targets and uses the observed difference to refine the freefall setting for future drops. The system learns when to begin closing the gate based on previous results.

Material conditions, temperature, gate response, silo geometry, and scale stability can still affect the outcome. Auto-learning provides better information, not a guarantee that every load will land at an exact weight.

Finer control for small drops

In 2026, Fastweigh expanded this approach with optional small-drop Pulse Mode. It uses recent product-specific history to estimate material flow and calculate a short gate-open time near the end of a load.

Auto-calculated freefall learns when to begin closing the gate. Pulse Mode learns how briefly to reopen it when only a small amount remains.

Both operate within the larger Fastweigh workflow, where the truck, order, product, target weight, load progress, and final ticket stay connected.

The result is a loadout process that uses previous performance to help operators control the next drop while maintaining a complete record of the load.

Learn more about Fastweigh asphalt silo loadout and ticketing.

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