Optimization 글 편집 글 편집 (이전 에디터)

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.

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