Study on Structural Lightweight Design of Automotive Front Side Rail Based on Response Surface Method

2006 ◽  
Vol 129 (5) ◽  
pp. 553-557 ◽  
Author(s):  
Y. Zhang ◽  
P. Zhu ◽  
G. L. Chen ◽  
Z. Q. Lin
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Lightweight Design ◽  
Front Side ◽  
Absorbed Energy ◽  
Entire Structure ◽  
Surface Method ◽  
Side Rail ◽  
Structural Sheet ◽  
The Relationship

Nowadays, vehicle lightweight design is a main topic in automotive industry. Crashworthiness, which is the most important performance of a full vehicle, must be always satisfied in the study on body lightweight design. This paper presents research, from the point of view of safety, of structural lightweight design of the front side rail of a passenger car. The response surface method is used to create mathematical models that represent the relationship between structural sheet thicknesses and absorbed energy of the entire structure in the frontal crash simulation, and the relationship between structural sheet thicknesses and the mass of the entire structure. Then an optimization process is performed, and the structural mass and original absorbed energy are defined as objective and constraint functions, respectively. Minimum mass and structural sheet thicknesses are obtained with the satisfaction of original absorbed energy of the front side rail structure. The weight reduction of the front side rail is 26.95%.

Goal-Driven Optimization Design of Lathe Bed Based on the Response Surface Method

2013 ◽  
Vol 433-435 ◽  
pp. 2208-2212
Author(s):  
Ming Cong ◽  
Hong Chuan Wang ◽  
Shan Shan Li ◽  
De Sheng Wang
Keyword(s):  
Machine Tool ◽  
Response Surface ◽  
Response Surface Method ◽  
Optimization Design ◽  
Lightweight Design ◽  
Engineering Application ◽  
Cnc Machine ◽  
Nc Machine Tool ◽  
Surface Method ◽  
Nc Machine

In this article, the Lathe Bed of DL20-MST CNC machine tool is taken as the research object. The response surface method and optimization design of goal-driven are introduced. Combining the two methods, the size optimization design method of the Lathe Bed of NC machine tool is put forward based on the ANSYS. The design dimensions which play an important role in the static and dynamic performance of Bed are found through sensitivity analysis. And then, the goal-driven optimization design of the Lathe Bed is carried out based on the response surface method, searching for the optimal dimensions of Lathe Bed, realizing the lightweight design of the Lathe Bed of NC machine tool. In addition, the improvement on the performance of Lathe Bed after optimization is analyzed. The ideas and methods used in this paper offer a good reference for the design and production of the key parts of other similar machine tools, and have a great engineering application value.

A Proposal of PSO with Presumption Function of the Solution Landscape by Response Surface Method

2014 ◽  
Vol 134 (9) ◽  
pp. 1293-1298
Author(s):  
Toshiya Kaihara ◽  
Nobutada Fuji ◽  
Tomomi Nonaka ◽  
Yuma Tomoi
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Surface Method

scholarly journals Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression

Materials ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 3552 ◽  
Author(s):  
Chun-Yi Zhang ◽  
Jing-Shan Wei ◽  
Ze Wang ◽  
Zhe-Shan Yuan ◽  
Cheng-Wei Fei ◽  
...  
Keyword(s):  
Neural Network ◽  
High Pressure ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Tip Clearance ◽  
Strong Nonlinearity ◽  
Surface Method ◽  
Blade Tip ◽  
Blade Tip Clearance

To reveal the effect of high-temperature creep on the blade-tip radial running clearance of aeroengine high-pressure turbines, a distributed collaborative generalized regression extremum neural network is proposed by absorbing the heuristic thoughts of distributed collaborative response surface method and the generalized extremum neural network, in order to improve the reliability analysis of blade-tip clearance with creep behavior in terms of modeling precision and simulation efficiency. In this method, the generalized extremum neural network was used to handle the transients by simplifying the response process as one extremum and to address the strong nonlinearity by means of its nonlinear mapping ability. The distributed collaborative response surface method was applied to handle multi-object multi-discipline analysis, by decomposing one “big” model with hyperparameters and high nonlinearity into a series of “small” sub-models with few parameters and low nonlinearity. Based on the developed method, the blade-tip clearance reliability analysis of an aeroengine high-pressure turbine was performed subject to the creep behaviors of structural materials, by considering the randomness of influencing parameters such as gas temperature, rotational speed, material parameters, convective heat transfer coefficient, and so forth. It was found that the reliability degree of the clearance is 0.9909 when the allowable value is 2.2 mm, and the creep deformation of the clearance presents a normal distribution with a mean of 1.9829 mm and a standard deviation of 0.07539 mm. Based on a comparison of the methods, it is demonstrated that the proposed method requires a computing time of 1.201 s and has a computational accuracy of 99.929% over 104 simulations, which are improvements of 70.5% and 1.23%, respectively, relative to the distributed collaborative response surface method. Meanwhile, the high efficiency and high precision of the presented approach become more obvious with the increasing simulations. The efforts of this study provide a promising approach to improve the dynamic reliability analysis of complex structures.

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