Mechanical Modeler Add-on Expands 3DCS to Support Kinematic Joints and Constraints

Extracting Joints and Constraints from CAD can make model creation easy. With embedded GD&T (PMI, FTA) tolerancing your model and Joints and Constraints brought in as 3DCS Moves, a user only has to create Measurements to complete their Tolerance Analysis Model. 

Mechanical Modeler is an inexpensive add-on for all versions of 3DCS Variation Analyst Software

This tutorial will cover  Fixed Constraints and  Fix Together Constraints to illustrate common constraints you can expect to use. The Fixed Constraint holds all of the parts in the Move Parts list in their current location and orientation. Any moves that list one of the parts as the "object" will force the "target" part to move instead. The Fix Together Constraint holds all of the parts in the Move Parts list perfectly rigid relative to one another. If one of the parts is moved then the other parts will move with it. Additional constraints include Coincidence, Contact, Offset, and Angle.

This tutorial will cover Revolute Joints and Cylindrical Joints to illustrate common joints you can expect to use. The Revolute Joint move locates two axes to one another controlling four DoFs (degrees of freedom) and uses a pair of plane constraints to control a fifth, translation, DoF. The Cylindrical Joint move locates two axes to one another controlling four DoFs. Additional joints include Prismatic, Spherical, and Planar.

Six-Sigma Programs - Using Defects Per Million Opportunities in 3DCS 

Defects Per Million Opportunities (DPMO) is a very important criterion used in Six Sigma programs as an objective and determinant for success or failure. How does DPMO relate and benefit Tolerance Analysis? 

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How Many Simulations Should I Run to Know That I've Run Enough?

Say that five times fast. Seriously, though, how many simulations are enough to be confident that your results are accurate?

 Edited 2-11-2015 for clarity

DCS recommends running 5000 to 20,000 simulations when analyzing a model.

Here is why:

Statistics are estimates of the parameters of a population. 3DCS results are statistics based on a sample (the number of simulations run) of an infinite population (the number of simulations that could be run). Because a statistic is an estimate, the confidence interval is used to determine how good an estimate it is. The confidence interval is calculated from the sample's size and standard deviation and the chosen confidence level (typically 90%, 95%, or 99%).

Why Does the Confidence Interval for My Dimensional Analysis Matter?

Confidence intervals provide information about the relationship between the actual statistic for a given simulation and the expected value of the parameter if a simulation had an infinite number of runs.