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Deterministic engine — same inputs, same result

Turn industrial
inputs into
defensible decisions.

Deterministic calculators for engineering, machining, quality and costing. See every input, assumption, formula and warning before you act. Client-side — your data never leaves your browser.

No login to try No data uploaded Reproducible by seed
Computing
Spec ± mm
Worst-case
RSS
Monte Carlo
Live preview — full Monte Carlo, Cpk & PDF in the calculator.
394
Automated tests, all passing
Decimal
Full-precision engine, no float drift
100%
Client-side — nothing uploaded
Seeded
Reproducible Monte Carlo

Flagship
Statistical tolerance stack-up

The hardest everyday question on the floor — will the assembled dimensions stay within spec? Three methods, one deterministic engine.

Engineering preview · not for production approval

Three methods, one answer you can trace

Worst-case for the safe bound, RSS for the statistical estimate, seeded Monte Carlo for the distribution — compared side by side, with the contributor that drives the most variation called out.

  • Per-part distribution: normal, uniform, truncated-normal, triangular
  • Real sample histogram — not a decorative bell curve
  • Predicted Cpk & PPM with a 95% confidence interval
  • Deterministic: same inputs + seed + engine version = same report
  • Save / load projects, CSV import, revision compare, PDF trace
Open the calculator →
SC-008 · PREDICTED ANALYSIS ENGINE V1.0.0
Worst-case±0.1150 mm
RSS±0.0687 mm
Monte Carlo (n=10,000)±0.0671 mm
Predicted Cpk2.18
Top contributorSpacer Width · 53%
Predicted in spec — verify with measured data before release

The set
Cost, quality and risk — one engine

Every tool shares the same principles: structured inputs, full-precision math, visible assumptions, and a result you can defend.

$

Cost & quoting

Know the real number before you commit a price or a hire.

  • SC-010 True labor cost
  • SC-012 Quote pricing & margin
σ

Quality & capability

Predicted capability from tolerances — flagged as prediction.

  • SC-008 Tolerance stack-up
  • SC-001 Weld thickness
!

Risk & review

See the contributor, the margin and the warning before it costs you.

  • Pareto Variation contribution
  • What-if Sensitivity, recomputed

Method
How a SectorCalc result is built

A result is only useful if you can defend it. Every report carries four layers — so a reviewer can follow the number from input to decision.

01

Recommended action

Primary result, pass / warning / fail, and the operational or commercial impact — stated up front.

02

Inputs & formula

Every input with units, derived values, the formula used and the assumptions made. Nothing hidden.

03

Checks & warnings

Input consistency checks, outlier warnings, the applicable standard and edition, and the engine version.

04

Audit trail

Executive result, detailed calculation, deviations, deterministic hashes and a PDF / shareable record.

Evidence
What a report actually looks like

Not a black-box number. A traceable record — the kind you can hand to a reviewer or attach to a design file.

SC-008 · Predicted Analysis

Calc ID SC008-7A3F1C9E (from inputs)
Engine SC008-2026.07-formula-v1.0.0+dist
Input hash 7a3f1c9e · Output hash 4d8b2e61
Method worst + RSS + seeded MC (n=10,000)
Generate your own →
CALC ID SC008-7A3F1C9E INPUT 7a3f1c9e / OUTPUT 4d8b2e61
Worst-case±0.1150 mm
RSS±0.0687 mm
Monte Carlo±0.0671 mm
Predicted Cpk2.18
Predicted PPM (95% CI)3 · 0–11
Top contributorSpacer Width · 53%
Predicted in spec — verify with measured data before release

What you get
Built to be defended, not just displayed

We don't claim to replace measured data or a licensed engineer. We claim to make the calculation transparent, repeatable and reviewable.

Every input visible

Structured, unit-aware inputs with tooltips that state the assumption — no hidden defaults.

fx

The formula shown

The method and the math are stated in the report, so a reviewer can follow the derivation.

!

Warnings flagged

Out-of-range inputs, thin margins and out-of-spec predictions are called out, not buried.

Reproducible record

Deterministic seed + engine version + hashes mean the same inputs reproduce the same report.

Standards & honesty
What we reference — and what we are not

Trust comes from stating limits as clearly as capabilities.

Referenced methodology

  • ISO 286-1Tolerance grades — reference for deviations
  • ASME Y14.5Statistical tolerancing context (RSS)
  • AIAG SPCCpk definition — model-derived here, not measured
  • AWS D1.1 / EN ISO 2553Weld sizing context
  • EngineSC008-2026.07-formula-v1.0.0+dist · 394 tests

How to read our results

  • Validated against closed-form worst-case / RSS invariants
  • Calculated under stated distribution assumptions
  • Applicable within 1D linear stack-ups
  • Predicted capability — not observed process capability
  • Reproducible from inputs + seed + engine version

Known limitations — stated on purpose

  • 1D linear stacks only. Per-part sigma is derived from the tolerance (no measured-process input yet). No correlation model between dimensions yet.
  • Results are predicted from drawing tolerances — an engineering preview to support a decision, not a substitute for measured SPC or a licensed engineer's sign-off, and not for production approval on their own.
Pricing

Pay with credits.
Only for what you use.

No subscription. Try every tool free first; open a premium calculation with credits valid for 12 months. Heavy users save with big packs; light users pay little.

See credit pricing →
No login · no upload · seeded

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