Estimate assembly variation from multiple component tolerances using worst-case or RSS
methods, then translate the result into capability impact. An optional Monte Carlo mode
simulates expected assembly variation under normal process behavior.
Analyzer
Enter component nominal values and bilateral tolerances
Core logic:
Worst-case = sum of tolerances, RSS = sqrt(sum of tolerance^2)
Use one component per line in the format: `Name, Nominal, +/-Tolerance`. The app assumes centered bilateral tolerances.
How to read the result
Worst-case is conservative and suitable when every tolerance can align in the same direction.
RSS is more realistic when component variation is random and centered, especially for mature processes.
Monte Carlo simulation assumes each component tolerance is approximately equivalent to +/-3 sigma unless actual process data says otherwise.
Components
Component contribution table
Component
Nominal
Tolerance
Worst-case share
RSS share
Simulation
Monte Carlo summary
Simulated mean: 47.750
Simulated sigma: 0.048
Observed simulated range: 47.570 to 47.935
Estimated out-of-spec rate: 0.02%
Instructions
How to use this app
Enter each component that contributes to the final assembly dimension, including its nominal
value and bilateral tolerance. The analyzer sums nominal values, computes worst-case and RSS
stack-up, and compares the selected result to the assembly specification.
Use worst-case when every tolerance must be guaranteed regardless of variation alignment.
Use RSS when component variation is random, centered, and reasonably stable.
The Monte Carlo option adds a probabilistic view. It treats each component tolerance as a
centered distribution and simulates repeated builds to estimate actual assembly spread and
likely spec escape.
What This Tolerance Stack-Up Tool Helps You Evaluate
This analyzer helps engineers estimate how component tolerances accumulate at the assembly
level. It supports worst-case and RSS-style thinking, plus variation simulation, so teams
can see how part-level uncertainty affects fit, function, and downstream capability.
Use it for design reviews, tolerance studies, launch readiness, and problem-solving work
where assembly fit looks unpredictable despite seemingly acceptable individual dimensions.
Core Stack-Up Logic
Method
Logic
When to Use It
Worst case
Sum all component tolerances directly
Use when every extreme must be protected explicitly.
RSS
Square root of summed squared tolerances
Use when variation is expected to combine statistically rather than stack perfectly.
Monte Carlo
Simulated distribution of assemblies
Useful when the team wants a more realistic variation picture.
Worked Example
An assembly dimension may depend on four components, each with its own tolerance. If the
full worst-case sum exceeds the assembly window, the design may be impossible to build
reliably. If RSS and simulation show acceptable spread, the team still needs to confirm
that the assumptions behind statistical independence are valid.
The tool helps make that design conversation explicit instead of relying on vague intuition.
How to Interpret the Results
Worst-case failure: the design is exposed if all component extremes can occur together.
RSS acceptable but worst case poor: the design may depend on statistical assumptions rather than guaranteed fit.
Wide Monte Carlo spread: process capability at the assembly level may be weak even with acceptable nominal dimensions.
Small stack-up margin: the assembly is sensitive to supplier drift or process shift.
Tolerance Stack-Up Frequently Asked Questions
What is the difference between worst-case and RSS analysis?
Worst-case assumes all dimensions can hit the extreme together. RSS assumes the variation combines statistically and is usually less conservative.
Why use Monte Carlo simulation?
It provides a more realistic picture of how assemblies may distribute in practice when several tolerance contributors interact.
Can good component tolerances still create bad assemblies?
Yes. Several individually acceptable variations can accumulate into an unacceptable assembly result.
What is the most common stack-up mistake?
Reviewing part dimensions in isolation without checking how the full chain affects the functional assembly requirement.
When should this analysis be revisited?
Revisit it when the design changes, suppliers change, capability shifts, or field fit issues suggest the original assumptions were too optimistic.
