Simulation-Based Sensitivity

Analyze Non-Linear Models

Simulation Based Sensitivity is the next stage in the advancement of 3DCS analysis. We can now see into 3DCS model behavior in ways not possible before.  Besides the usual HLM lists, we now gain insight into multivariate interactions between inputs and their effects on output mean shift.  

Use SBS when:

• You find a nonlinear relationship
• You want more detailed information about how contributors affect your measure results
• You have a mean shift or want to account for one

Advanced Analyzer Matrix 

View Features From Across the Entire Model

The Advanced Analyzer Matrix gives you the ability to see all of your measurements or tolerances in a single matrix. You have the ability to change parameters and inputs, as well as change tolerances in the matrix to instantly see the resulting changes. 

Benefits:

• Global View of Model
• Customize for Outputs and Color Coding for Specific Criteria 
• Make Changes and See Instant Results

Quality Optimizer

Reduce Costs By Increasing Non-Critical Tolerances 

The Optimizers determine which tolerances are critical, and which are non-critical, and then increase non-critical tolerances to reduce manufacturing costs. This is a great method of taking general tolerances across an entire model, and optimizing them to have tighter tolerances on key features while opening (increasing) tolerances are non-critical areas, all with the push of a button. Not sure you want to keep the new tolerances? AAO only updates your model when you select the Push to Model button and update your model.

Benefits:

• Reduce manufacturing costs
• Alter tolerances across the model reflecting critical features
• Change the tolerances in your model only when you want to 

Locator Sensitivity Analyzer

Test and validate your locators. Create different locating patterns and simulate to find the pattern that results in the least variation. Use color mapping to make quick determinations about the optimal locator locations. 

Benefits:

• Validate your locators 
• Test different locators to determine the best choice
• Use color mapping to quickly make decisions

Critical Tolerance Identifier

Identify Tolerances Critical to Fit and Function

Use Monte Carlo simulation calculations to weigh the measures in the model to determine the percent contribution to the overall model. This determines your most critical measurements and tolerances, basically your largest contributors to variation across the entire model, instead of in relation to a specific measurement. 

Benefits:

• Find greatest contributors to overall model variation
• Use different statistics, Pp, Ppk, Cp, to determine the criteria
• Take advantage of previous calculations; no need to run new simulations