Fundamentals, Worst Cost, and Root Sum Square Approaches
nature of dimensioning and tolerancing.
Tolerance analysis purposes. Tolerance
analysis purposes. Tolerance
analysis history. How
tolerances are typically assigned. Tolerance
analysis concepts. Worst
case tolerance analysis and worst case tolerance analysis shortfalls.
Statistical tolerance analysis and statistical tolerance analysis
between worst case tolerance analysis and statistical tolerance analysis.
Suggested tolerance analysis approach selection criteria.
nature of variability. The
normal distribution. Means
and standard deviations. Manufacturing
process variability. Process
capability, Cp, and Cpk. Tolerances
and nominal dimensions versus process capability.
Coefficient incorporation to address differences in design nominal
and process nominal dimensions.
Tolerance Analysis Concepts. Statistical
tolerance analysis purposes. Statistical
tolerance analysis assumptions.
The realism of statistical tolerance analysis.
Maximum possible versus maximum probable dimensional variation.
Why statistical tolerance analyses predict less variation.
The economics of worst case tolerance analysis versus statistical
Root Sum Square
Statistical Tolerance Analysis.
Dimension chains, positive versus negative directions, and
converting to equal-bilateral format.
Finding the root sum square of all tolerances.
Knowing the manufacturing process and assembly shift, and
incorporating adjustment coefficients.
Applying statistical tolerance analysis findings for dimensional
statistical tolerance analysis for relaxing component tolerances.
Using Excel. Exercises.
Day 2: Monte Carlo and
Carlo Tolerance Analysis. The
Monte Carlo approach. Differences
in Monte Carlo simulation approaches.
Applying uniform versus normal distributions in the simulation.
Randomness and normal statistical variation.
Monte Carlo simulations with Excel and VBA for Excel.
Statistical tolerance analysis versus Monte Carlo tolerance
analysis considerations. Exercises.
Allocation Approaches. Typical
tolerance assignment approaches. Tolerance
allocation based on worst case, root sum square, and Monte Carlo tolerance
allocation incorporating the tolerance analysis approach and component
size, process capability, cost, and mean shift.
Using Excel for tolerance allocation.
Statistical Tolerance Analysis Applicability.
Number of tolerances. Production
quantities. Process controls
and process capability. Centered
processes versus nominal dimensions.
Design sensitivity. Interchangeability.
Independent variables. Suggested
Economics Considerations. Costs
and benefits associated with statistical tolerance analysis.
Costs associated with tighter versus looser tolerances.
Rejections as a result of statistical tolerance analysis
approaches. Using statistical
tolerance analysis to predict assembly rejection rates.
Targeting tolerance relaxation candidates.
weighting by individual tolerance. Risks
and risk management.
Course review. Questions
and answers. Plans for
future actions. Course