Tolerance Stack-up Analysis By James D. Meadows

Use Meadows’ decision matrix:

Without this analysis, teams resort to over-tolerancing (expensive) or under-tolerancing (risky). James D. Meadows dedicated his career to eliminating this dilemma. tolerance stack-up analysis by james d. meadows

| Type | Objective | Output | | :--- | :--- | :--- | | | To find the absolute maximum and minimum possible assembly variation, assuming all tolerances are at their extreme limits simultaneously. | Guaranteed assembly (100% yield theoretically) but often results in tight individual tolerances. | | Statistical (RSS) | To find a more realistic range of variation, assuming tolerances follow a normal distribution (e.g., ±3σ). | Allows looser tolerances, but with a small risk of non-assembly (e.g., 0.27% for ±3σ). | Use Meadows’ decision matrix: Without this analysis, teams

There is virtually no discussion of how to implement these calculations in modern tolerance analysis software (e.g., CETOL, 3DCS, Sigmetrix). It is strictly manual calculation methods. | Type | Objective | Output | |

This assumes every part in the assembly is at its most extreme tolerance limit simultaneously. It is the safest method for critical safety components but can lead to overly tight, expensive tolerances. Statistical Analysis (RSS): Root Sum Square (RSS)

Meadows provides tools for both Worst-Case analysis —assuming all parts are at their extreme limits—and statistical methods like Root Sum Squares (RSS) and the Bender Factor for high-volume production. Key Benefits of His Approach