Optimization 글 편집

Designing a mechanical system often involves optimizing the design variables with respect to specific performance metrics. RecurDyn provides a high performance optimization tool, AutoDesign, that requires very little knowledge of optimization to use because of its straightforward user interface.

Unique characteristics of AutoDesign

  • Easy and intuitive interface which allows anyone to use with a little practice
  • The world’s first progressive meta-model algorithm, motivated from Bayesian Global Optimization
  • Easy definition and customization of the design variables and objective functions
  • Robust design optimization techniques to consider uncertainties such as tolerances and noises
  • Multi-scale optimization techniques to solve the problems which have the different scales of design variables
  • Easy and powerful multi-objective optimization algorithm which can be used regardless of the number of objectives
  • Optimization with very small number of trials
  • For example, it used only 116 analyses to optimize a design that had 105 design variables and 14 performance indices.

Optimization using RecurDyn AutoDesign

Optimizing Dynamic Mechanisms using RecurDyn

Various features of AutoDesign

Design Study : Design Study provides 6 methods for DOE (Design Of Experiments)
  • Provides ways to perform DOE with the optimal number of samplings
  • 2-level and 3-level orthogonal array experiments are automatically generated according to the number of design variables.
  • Descriptive DOE which allows the users to define the level and the number of experiments
  • Eect analysis, screening variables and correlation analysis are supported.

Design Optimization : Design Optimization provides the functions for optimization of the system using the meta-model.

  • Progressive meta-model based on optimization technique is employed to reduce the number of trials (analyses).
  • Even beginner users can use optimization using automated methods.
  • Various options are supported for the experienced users.
  • The existing optimization results can be reused.
  • All difficult selections of optimization algorithms are automated.

DFSS/Robust Design Optimization : Optimization for DFSS (Design for Six Sigma) is supported.

  • Progressive meta-model based on optimization technique is employed to reduce the number of trials (analyses).
  • Approximate variance of performance during optimization process can be estimated.
  • Users can define the tolerance and deviation of random design variables and random noise.
  • Adaptive 6-sigma inequality constraints are considered unlike the other optimization tools which focus on only statistical dispersion.
  • User can define the robustness of objective functions.

Reliability Analysis : Revolutionary algorithm of Reliability Analysis can produce reasonable reliability results with a smaller number of samplings than the traditional methods.

  • SAO Hybrid Method: Powerful Reliability algorithm which is integrated with Progressive meta-model based on optimization techniques and MPP-based DRM (Dimension reduction Method)
  • Adaptive Monte-Carlo Method: New method which uses sequentially adaptive Monte-Carlo algorithm to minimize the number of sampling points