Automotive Fender Case Study for Datum Optimizer - A Genetic Algorithm Based Tool for 3DCS
by Paul Vickers, on Feb 3, 2021 10:50:46 AM
Datum Optimizer - A Genetic Algorithm Based Tool for 3DCS AAO Add-on
Example Case with Automotive Fender
For the rigid datum feature case, to validate the performance of DO, a step-plane (3:2:1 locating scheme) move was used to locate the fender to a fixture, and a normal distributed tolerance was applied on selected datum targets. As shown in Figure 1, the model with initially selected datum features produced a risk of 26% out-of-specification and a poor process performance value (Ppk = 0.13). After datum optimization, three optimal primary datum features were found. The model with optimal datum features produced an excellent result distribution with only 0.8% out-of-specification, meanwhile increasing the process performance value (Ppk) to 0.81.
Figure 3: A fender model with candidate and constraint points
Datum Optimizer is a tool to find the optimal datum features for manufacturing and assembly processes, which does not require any assembly moves or part tolerances on the model. It only requires a single part with a set of candidate points and FEA files if conducting compliant deformation. Using DO during the design stage and prior to production, users can eliminate or minimize the rework and tuning of mechanisms to select the proper datum features while retaining a high confidence in passing Gage R&R.
To achieve the goal of minimizing rigid part variation or compliant part deformation caused by gravity and clamp operations, DO uses a state-of-the-art genetic algorithm to efficiently find optimal datum features from a candidate set.
As shown in the example, DO not only shows users where the optimal locations are but also suggests when to stop adding more locators due to diminishing returns.