scholarly journals An Efficient Design of Experiments Using RBD-Fast

2021 ◽  
Author(s):  
S. Wenzel ◽  
E. Slomski-Vetter ◽  
T. Melz
Keyword(s):  
Design Of Experiments ◽  
Efficient Design

scholarly journals Statistical Design of Experiments: An introductory case study for polymer composites manufacturing applications

2021 ◽  
Vol 347 ◽  
pp. 00028
Author(s):  
Natasha Botha ◽  
Helen M. Inglis ◽  
Roelof Coetzer ◽  
F. Johan W.J. Labuschagne
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Polymer Composites ◽  
Response Surface ◽  
Statistical Design ◽  
Efficient Design ◽  
Statistical Design Of Experiments ◽  
Wide Range ◽  
Multiple Variables

Statistical design of experiments (DoE) aims to develop a near efficient design while minimising the number of experiments required. This is an optimal approach especially when there is a need to investigate multiple variables. DoE is a powerful methodology for a wide range of applications, from the efficient design of manufacturing processes to the accurate evaluation of global optima in numerical studies. The contribution of this paper is to provide a general introduction to statistical design of experiments for a non-expert audience, with the aim of broadening exposure in the applied mechanics community. We focus on response surface methodology (RSM) designs — Taguchi Design, Central Composite Design, Box-Behnken Design and D-optimal Design. These different RSM designs are compared in the context of a case study from the field of polymer composites. The results demonstrate that an exact D-optimal design is generally considered to be a good design when compared to the global D-optimal design. That is, it requires fewer experiments while retaining acceptable efficiency measures for all three response surface models considered. This paper illustrates the benefits of DoE, demonstrates the importance of evaluating different designs, and provides an approach to choose the design best suited for the problem of interest.

scholarly journals Efficient design of experiments in the Monod model

2003 ◽  
Vol 65 (3) ◽  
pp. 725-742 ◽  
Author(s):  
Holger Dette ◽  
Viatcheslav B. Melas ◽  
Andrey Pepelyshev ◽  
Nikolai Strigul
Keyword(s):  
Design Of Experiments ◽  
Monod Model ◽  
Efficient Design

Robust and efficient design of experiments for the Monod model

2005 ◽  
Vol 234 (4) ◽  
pp. 537-550 ◽  
Author(s):  
Holger Dette ◽  
Viatcheslav B. Melas ◽  
Andrey Pepelyshev ◽  
Nikolay Strigul
Keyword(s):  
Design Of Experiments ◽  
Monod Model ◽  
Efficient Design

Integrated method for performance analysis of reliability-based topologically optimized components

2019 ◽  
Vol 233 (19-20) ◽  
pp. 7019-7040
Author(s):  
Arshad Javed ◽  
Joshua Amrith Raj ◽  
BK Rout
Keyword(s):  
Topology Optimization ◽  
Design Of Experiments ◽  
Volume Fraction ◽  
Signal To Noise Ratio ◽  
Statistical Significance ◽  
Optimization Methods ◽  
Von Mises Stress ◽  
Efficient Design ◽  
Integrated Method ◽  
Design Factors

The available robust and reliable topology optimization methods provide quick and efficient design output in an uncertain environment. However, the whole domain of performance function remains hidden during this design process. In the interest of the designer, it is required to know the overall behavior of performance functions in deterministic as well as uncertain/realistic environment. The current work achieves this by proposing an integrated methodology, which combines the design of experiments approach and reliability-based topology optimization. The proposed method enables the designer to simulate performance functions in a desired design-factors space, including uncertainties, via reliability value. For this analysis, compliance, maximum deflection, mechanical advantage, and von Mises stress values are selected as performance functions. Volume fraction, applied force, and dimensions or aspect ratio are chosen as design/control factors. The uncertainties of these design factors are captured using reliability-based topology optimization. The uncertainties due to noncontrollable factors such as material property, load direction, and magnitude are incorporated using the design of experiments approach. Under these uncertainties, the performance of topologically optimized problem is simulated for different experimental combinations of the design factors. The experimental combinations for uncertainties and design factors are generated using Taguchi's orthogonal array. Simulated results are analyzed using techniques such as analysis of mean and variance, signal-to-noise ratio, and response surface method. These analyses help in identifying statistical significance of factors and uncertainties, performance variations, and equivalence relation of performance vs. factor. The proposed methodology is illustrated by selecting monolithic structures such as, on MBB, cantilever beam, and force inverter mechanism.

Sign in / Sign up

Export Citation Format

Share Document