RecurDyn AutoDesign is a sequential approximate design optimization tool. The development philosophy of RecurDyn AutoDesign makes the use of the optimization easy. AutoDesign provides efficient four design tools by automating all difficult mathematical background.

Various features of RecurDyn AutoDesign

Unique characteristics of RecurDyn AutoDesign

Easy and intuitive interface which allows anyone to use with a litte 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 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.

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

Effect analysis, screening variables and correlation analysis are supported.

Design Optimization : Design Optimization provides the function for optimization of the system using the meta model

Progressive meta model based on optimization techniques is employed to reduce the number of trials (analyses).

Even users who are beginner can use optimization using automated methods.

Various options are supported for the experienced users.

The existing optimization resuls 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 ffocus on only statistical dispersion.

User can define the robustness of objective functions.

Reliability Analysis : Revolutionary algorithm of Reliability Analysis can produce reasonalbe 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.