PPolySim OS
Use case · powered by Learning Curve

Learning Curve for an injection mold

Simulate an injection mold live in your browser. This runs the real Learning Curve solver — adjust the inputs, watch it respond instantly, and export the result. No install, no account.

Learning CurveLive

Controls

Presets

Every time cumulative production doubles, the cost per unit falls by a fixed percentage — the learning curve. An 80% curve means the 200th unit costs 80% of the 100th. This steady, predictable decline, first seen in aircraft manufacturing, drives the falling prices of everything from solar panels to microchips, and underpins production planning and pricing.

▶ Run in Python

Data Inspector

Cost of unit N$22.7
Learning rate80%
Cost reduction77%

Governing equation

Reading this result: An 80% curve means every doubling of output trims 20% off the unit cost — so by unit 100 you are paying about $23, a 77% drop from the first unit. Lower rates bend the curve down faster.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

or unlock everything with Pro →

About this simulation

The full Learning Curve tool models an injection mold with the same numerics engineers and scientists use — running entirely client-side. Change any parameter and the result updates in real time, so you can build intuition, check a design, or teach the concept without spreadsheets or installs.

More you can do with Learning Curve

Other ways to simulate an injection mold

Frequently asked questions

How do I simulate an injection mold?
Open this page and use the live Learning Curve tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.
Is it free?
Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.
Can I use my own numbers?
Absolutely — every input is adjustable, and with data import you can drive an injection mold from your own measurements.