To evaluate how system performance will be affected by manufacturing tolerances, and help you to find the best balance between cost and performance, OSLO includes state of the art tolerancing tools:
Surface Tolerances describe the quality of each surface:
Component Tolerances describe the positioning of surfaces relative to one another:
Group Tolerances allow you to set up subsystems or elements of your overall design:
Allows you to specify an error function to characterize system performance. This method provides the greatest flexibility for tolerancing, but has the slowest performance. Since this method is based on an error function, you can select which terms to display, so that only relevant data is shown.
Change table tolerancing
This tool tells you how specific performance aspects of your system (see list below) will be affected by tolerances. It has the advantage of not requiring an error function to be defined.
This method uses the Hopkins-Tiziani algorithm, which allows you to tolerance a system based on an MTF or RMS wavefront evaluation. Although defining an error function and using User-defined tolerancing can also achieve this, MTF/Wavefront tolerancing may be as much as 100 times faster.
Monte Carlo Tolerancing
It is the closest thing to real world simulation. A number of systems are statistically generated and evaluated, giving you an accurate idea of what your rate of success is going to be.
OSLO includes features that simplify the interpretation of tolerance data:
A grade table (A, B, C, D), establishes ranges for which a tolerance is considered Very Tight (A), Tight (B), Standard (C), or Loose (D).
A display threshold can be used to suppress unimportant output.
Sorting routines allow you to find out at a glance which tolerances are critical.
OSLO allows you to chose from different tolerancing methods. Each method can be used for either analysis ("sensitivity") or design ("inverse sensitivity"), with the exception of Monte-Carlo (sensitivity only):