Understanding 3DCS Datum Optimizer Functions for Rigid or Compliant Parts
by Benjamin Reese, on Jan 21, 2021 3:51:25 PM
Understanding 3DCS Datum Optimizer Functions
3DCS Datum Optimizer (DO) is a tool that uses a genetic algorithm (GA) to determine the optimal datum features from a candidate set with the goal of minimizing the rigid part variation or compliant part deformation. Using DO during the design stage and prior to production gives users the ability to eliminate or minimize rework, reduce tuning mechanisms to select the proper datum features, and have a higher confidence in passing Gage Repeatability and Reproducibility (R&R) studies.
3DCS Datum Optimizer Functions
Primary datum locators for rigid parts
Optimal clamp minimization for compliant parts
Datum Optimizer objectives
DO has two simulation types - Genetic Algorithm-based optimization or full-factorial simulation. Full-factorial simulation guarantees that users can find global optimal (Best) datum features, but requires more computational resources. The number of possible combinations of n candidates taken r at a time is Cnr =n!/(r!(n-r)!). The number of simulations grows dramatically as the number of candidates and number of datums grows. As shown in Table 1, finding 3 optimal primary datums out of 80 candidates, full-factorial simulation requires 82,160 runs. The Genetic Algorithm-based optimization limits the maximum number of simulations to the population size times the number of generations. With default parameters set, DO only requires 13,144 runs, which is 16% of the full-factorial runs, while having more than a 90% possibility to find the global optimum. This means that you can use only 16% of the computational power to get a 90% chance of success in finding the global optimum. Even though the Genetic Algorithm-based optimization method has an excellent gain-cost ratio, full-factorial simulation is recommended if computational resources are not a concern for you.
Table 1: Comparison of number of runs of full-factorial and Genetic Algorithm-based optimization
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