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Datum Optimizer Whitepaper

Datum Optimizer  

A Genetic Algorithm-Based Tool for 3DCS Advanced Analyzer and Optimizer AAO Add-on

Zesheng Zhang, 3DCS Developer DCS

Maria Harangus, Sr. Dimensional Engineer DCS

Gary Bell, Sr. Dimensional Engineer DCS

Paul Vickers, Sr. Dimensional Engineer DCS

Ben Reese, Director of Marketing DCS


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 higher confidence in passing Gage Repeatability and Reproducibility (R&R) studies.

Improper selection of datum features during manufacturing and assembly may cause severe quality problems. Improper datum features risk turning all the quality parts, accurate dimensioning and tolerancing, and first-class operations into a defective assembly.  

Datum Optimizer is an easy-to-use tool in 3DCS Advanced Analyzer and Optimizer Add-on, which does not require creating moves (Assembly Definition) or tolerances on the model. For a rigid part, it only requires a single CAD part with a set of candidate points. For a compliant part, besides the part and candidate points, it additionally requires the 3DCS FEA Compliant Modeler Add-on and Finite Element Analysis (FEA) files to be ready as it involves FEA calculations.

This white paper discusses the background of Datum Optimizer in the following section. Two functionalities of DO are described in the third section, followed by a brief description of the genetic algorithm. To illustrate the process, an example model is included in the fourth section. Finally, the results are discussed in the conclusion as well as further planned development. 


  • Executive Summary 2
  • Why Selecting Proper Datums is Critical 4
  • Understanding 3DCS Datum Optimizer Functions 5
  • Primary datum locators for rigid parts 5
  • Optimal clamp minimization for compliant parts 5
  • Datum Optimizer Objectives 6
  • Simulation type 6
  • Genetic algorithm (GA) 7
  • Example Case 9
  • Conclusion 12
  • Future Outlook 13
  • References 13 



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