Implementation of a novel Six Sigma multi-objective robustness optimization method based on the improved response surface model for bumper system design

2021 ◽  
Vol 167 ◽  
pp. 108257
Author(s):  
Hangyan Wang ◽  
Guangguang Zhang ◽  
Shuiting Zhou ◽  
Liange Ouyang
Keyword(s):  
System Design ◽  
Response Surface ◽  
Six Sigma ◽  
Optimization Method ◽  
Response Surface Model ◽  
Surface Model ◽  
Multi Objective ◽  
Robustness Optimization ◽  
Objective Robustness ◽  
Bumper System

Finite Element Analysis and Multi-Objective Optimization of the Precision Horizontal Machining Center Bed

2014 ◽  
Vol 889-890 ◽  
pp. 130-134
Author(s):  
Xue Yan Li ◽  
Wen Tie Niu ◽  
Jun Qiang Wang ◽  
Ling Jun Xue
Keyword(s):  
Response Surface ◽  
Response Surface Model ◽  
Least Square ◽  
Surface Model ◽  
Multi Objective Optimization ◽  
Element Analysis ◽  
Maximum Deformation ◽  
Multi Objective ◽  
Machining Center ◽  
Sample Points

In order to improve dynamic and static performance of the precision horizontal machining center, the method of multi-objective optimization based on the response surface model was applied for optimizing design of the bed structure. The design variables were the layout parameters of the rib plates. Sample points were obtained by the Box-Behnken design experiment, and responses of sample points were analyzed by SAMCEF. The maximum deformation of guide rails and the low-order natural frequency were extracted to fit the response surface model by least square method. The layout parameters of the rib plates were optimized through the application of multi-objective genetic algorithms. Then, relationship between the lightening holes and the performance were analyzed to determine the suitable diameter. The results verify the validity of the optimization method, and the paper provides methodological guidance for optimization of machine tool structural parts.

Multi-Objective Aerodynamic Design Optimization Based on Camber Line and Thickness Distribution for a Transonic Compressor Rotor

2008 ◽  
Author(s):  
Xiangfeng Wang ◽  
Songtao Wang ◽  
Wanjin Han
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Response Surface ◽  
Total Pressure ◽  
Optimization Problems ◽  
Thickness Distribution ◽  
Response Surface Model ◽  
Navier Stokes ◽  
Surface Model ◽  
Multi Objective

The paper describes a new optimization system for computationally expensive design optimization problems of turbomachinery, combined with design of experiment (DOE), response surface models (RSM), multi-objective genetic algorithm (MOGA) and a 3-D Navier-Stokes solver. A flow field solver code was developed based on three dimensional Navier-Stokes equations and validated by comparing computation results with experimental data. The improved non-dominated sorting genetic algorithm (NSGA-II) was used to solve the multi-objective problems. A constraint handling method without penalty function used to treat constrained optimization problems was improved and applied to constrained multi-objective problems. Data points for response evaluations were selected by the improved-hypercube sampling (IHS) algorithm and 3-D Navier-Stokes analysis was carried out at these sample points. The quadratic response surface model was used to approximate the relationships between the design variables and flow parameters. The genetic algorithm was applied to the response surface model to perform global optimization and obtain the optimum design. The above optimization method was applied to aerodynamic redesign of NASA Rotor37 with camber line and thickness distribution, the objects were to maximize the total pressure ratio and the adiabatic efficiency. Results showed the adiabatic efficiency improved by 0.7% and the total pressure by 0.66%. The multi-objective optimization design method is feasible.

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