Get Started with Templates
For a two-part stack-up, assume shared datums and simply add both feature tolerance analyses.
| # | From | To | Mfg Process | Nominal (mm) | + Tol (mm) | − Tol (mm) | Dir | Actions |
|---|---|---|---|---|---|---|---|---|
| 1 | A: | B: | ||||||
| 2 | B: | C: | ||||||
| 3 | C: | D: | ||||||
| 4 | D: | E: | ||||||
| 5 | E: | F: | ||||||
| 6 | F: | G: | ||||||
| 7 | G: | Aʼ: |
All features assume a normal (Gaussian) distribution for RSS and Monte Carlo analysis. This is standard practice because manufacturing dimensions follow a bell curve around the nominal per the Central Limit Theorem.
Recommendation: Use RSS
With 7 contributors, RSS analysis is a reasonable approach for production scenarios. Use worst-case only for safety-critical or low-volume applications where 100% conformance is required.
RSS assumes independent, normally-distributed feature dimensions. Verify this with process capability data (Cpk ≥ 1.33). For critical assemblies — medical, aerospace, or safety-related — worst-case is the conservative, industry-accepted default.
Results
Worst Case
+0.175
−0.175
Sum of all tolerances (mm)
RSS (4σ)
+0.066
−0.066
Root Sum Square (mm)
DPPM
Set target to calculate
Defective parts per million
Yield
Set target to calculate
Expected manufacturing yield
Worst Case vs RSS Comparison
RSS is 62% tighter than worst case
Sensitivity Analysis
Each feature's percentage contribution to total RSS variance. Focus design effort on the top contributors.
Tolerance Allocation
Reverse-solve: given a target tolerance and desired yield, compute what each feature's tolerance should be.
Set a +Target and/or −Target above to calculate allocation.
Monte Carlo Simulation
Click “Run” to simulate 1M random assemblies and visualize the stack-up distribution.
Optional: Upload Historical Data
Upload an Excel (.xlsx) or Numbers file with at least 500 rows for reliable results. Format: first row must be column headers 1, 2, 3, ... matching the number of features (7). Each column contains measured values in mm.
Select a data type above to enable file upload.
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References & Resources
Tolerance Analysis Theory
- ASME Y14.5-2018— The definitive U.S. standard for GD&T
- ISO 8015 — GPS Fundamentals— International tolerancing standard
- Drake — Dimensioning & Tolerancing Handbook— Comprehensive tolerance analysis reference
- Creveling — Tolerance Design— RSS, Monte Carlo, and Six Sigma methods
Monte Carlo Simulation
- Enventive — Worst Case, RSS & Monte Carlo for Stackups— Clear walkthrough of all three methods with real examples
- Minitab — Understanding Tolerance Stack-Up Analysis— Beginner-friendly overview of worst-case vs Monte Carlo
RSS assumes each feature tolerance represents a ±4σ distribution (99.9937% coverage).
DPPM and yield are based on the RSS stack-up compared to the target tolerance.