RecurDyn Optimization
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 deffnition 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.
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
- Efficient 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