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Measurement & Tolerance Workspace

Evaluate repeated measurements, build an uncertainty result, and make a conformity decision—or switch to a dimensional tolerance stack. Both modes run locally and reuse existing verified engines.

Statistics-backed workflow

Measurement → uncertainty → decision

Enter repeated observations and one or more Type B contributions. The workflow calculates Type A statistics, combines standard uncertainty by RSS, expands it by k, then applies a simple or guarded conformity rule.

Conformity decision
EXAMPLE DATA · NOT A DECISION RECORD
INCOMPLETE

Complete the inputs and run the workflow. No measurement data leaves this browser.

MeasurementsStatisticsUncertaintyDecision
Mean—
U—
Guarded acceptance zone—
Raw specification—

Measurement model

Guarded mode uses the calculated expanded uncertainty U = k·uc as the guard width. It is a binary ILAC G8-style rule, not a JCGM 106 risk model.

Comma, space, or line separated; at least two values.
ComponentSourceHalf-width aDistributionSensitivity cUnitStandard uncertaintyActions
Independent components only. Correlated components require GUM propagation. Maximum 32 rows.
Evaluation remains editable. Export creates a timestamped JSON decision record with a SHA-256 digest.

Result details

Statistics and standard-uncertainty composition for the evaluated mean.

n—
s—
uA—
uB total—
uc—
Decision basisMean

Method & evidence

Workflow v1.2.0 · 3 versioned contracts

Local evaluation

Inspect versioned Evidence: Type A · Type B · Decision rule

How this workspace works

One local scenario holds a measurement decision and a saved 1-D stack.

1

Evaluate measurements

Start with repeated observations, build a Type B uncertainty budget, and define specification limits. The result records each engine step instead of hiding the path behind a single number.

2

Edit the stack

Use the independent dimension-stack path when the task is a design tolerance problem. From Stack-up, Worst-case, or Monte Carlo, click Add to workspace. Nothing leaves this browser.

3

Keep the model boundary visible

The measurement path assumes independent Type A and Type B components. Correlated GUM propagation stays on Uncertainty propagate; distributions and yield stay on Monte Carlo.