An EPC Contract Parameter Optimization Method Based on the Response Surface Method: A Case Study of a Metro in China

2021 ◽  
Author(s):  
Zongbao Feng ◽  
Xianguo Wu ◽  
Hongyu Chen ◽  
Limao Zhang ◽  
Miroslaw J. Skibniewski
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Parameter Optimization ◽  
Optimization Method ◽  
Surface Method

Parameter Optimization of High Deposition Rate Laser Cladding Based on the Response Surface Method and Genetic Neural Network Model

2021 ◽  
Vol 48 (6) ◽  
pp. 0602112
Author(s):  
庞祎帆 Pang Yifan ◽  
傅戈雁 Fu Geyan ◽  
王明雨 Wang Mingyu ◽  
龚燕琪 Gong Yanqi ◽  
余司琪 Yu Siqi ◽  
...  
Keyword(s):  
Neural Network ◽  
Response Surface ◽  
Response Surface Method ◽  
Deposition Rate ◽  
Network Model ◽  
Parameter Optimization ◽  
Neural Network Model ◽  
High Deposition Rate ◽  
Surface Method ◽  
Genetic Neural Network

A Two-Stage Optimization Method for the Stencil Printing Process Based on Neural Network and Response Surface Method

2010 ◽  
Vol 156-157 ◽  
pp. 10-17 ◽  
Author(s):  
Er Shun Pan ◽  
Yao Jin ◽  
Zhao Mei ◽  
Ying Wang
Keyword(s):  
Neural Network ◽  
Response Surface ◽  
Response Surface Method ◽  
Model Fitting ◽  
Optimization Method ◽  
Small Sample ◽  
Two Stage ◽  
Stencil Printing ◽  
Printing Process ◽  
Surface Method

A stencil printing process (SPP) optimization problem is studied in this paper. Due to the limitation that neural network requires a large number of samples for the accurate model fitting, a two-stage SPP optimization method is proposed. The design interval can be reduced with small sample by using neural network. In this reduced design interval , response surface method is adopted to obtain the accurate mathematical SPP model. The concept of confidence level is introduced to make the proposed model robust. An interactive method is used to solve the model. The proposed method is compared with the one-stage optimization method and the results show that the proposed method achieves a better performance on each objective.

Research of Revised Thermal Design Parameter Optimization With Response Surface Method Application for Fitness Function

2004 ◽  
Author(s):  
Tohru Nakanishi ◽  
Keishi Okamoto ◽  
Koji Koyamada ◽  
Hidetoshi Kotera
Keyword(s):  
Numerical Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Parameter Optimization ◽  
Temperature Difference ◽  
Fitness Function ◽  
Thermal Design ◽  
Compact Modeling ◽  
View Point ◽  
Surface Method

For the thermal management of the electric products, the compact modeling method is commonly applied to the numerical analysis with the simplification of the each component used in the products, on the view point of the best thermal design. In the generation of the compact modeling, the method that the model parameters are optimized toward to the fitness between the temperature value of numerical analysis result and the actual hardware testing data, is conventionally used, with some monitored points which are given in advance. In this parameter optimization, the fitness function is to be 1 on the no (0) temperature difference between them at all monitored locations, and on the other hand, that is to be 0 if the temperature difference between them is infinite. However, it was found that this conventional method brought the fitness function shape of one sharp mountain, as the analysis result, and the method, that represented the fitness function with the quadratic polynomial, caused the important problem on the view point of the analysis quality if the Response Surface Method is used of the parameter optimization. Therefore in this research to resolve this problem, we suggest the method that the response surfaces, which are based on the heat conduction law, are composed for each monitored location, and the fitness function is given with these surfaces. This method is applied into some compact modeling and the benefit is verified. The meanwhile of the temperature difference between the numerical analysis result and the solution field is decreased half, and the divergence makes one-tenth decrease.

Research on Vibration Parameters of Tamping Operation

2015 ◽  
Vol 724 ◽  
pp. 283-286
Author(s):  
Tao Yong Zhou ◽  
Bin Hu ◽  
Yong Xin Peng ◽  
Bo Yan
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Optimization Method ◽  
Analysis Method ◽  
Important Work ◽  
Railway Ballast ◽  
Surface Method ◽  
Vibration Parameters ◽  
Railway Maintenance ◽  
Effective Analysis

Tamping operation is an important work of railway maintenance. There are many factors that can influence the tamping effect. This paper mainly studies the influence of vibration frequency and amplitude on the porosity of railway ballast. In order to obtain the minimal porosity, the response surface method was used to seek the optimal vibration parameters. Using an optimization method, the optimum vibration parameters were obtained that gives the minimal porosity. The obtained results are consistent with those of other similar studies, which mean that the response surface method is an effective analysis method of multi-parameter optimization problem under tamping operation.

Gear-tolerance optimization based on a response surface method

2018 ◽  
Vol 42 (3) ◽  
pp. 309-322
Author(s):  
Rui Xu ◽  
Kang Huang ◽  
Jun Guo ◽  
Lei Yang ◽  
Mingming Qiu ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Response Surface ◽  
Response Surface Method ◽  
Tolerance Analysis ◽  
Optimization Method ◽  
Small Displacement ◽  
Tooth Contact Analysis ◽  
Tolerance Optimization ◽  
Surface Method ◽  
Low Efficiency

To address the low efficiency of gear-tolerance analysis and optimization, a gear-tolerance optimization method based on a response surface method (RSM) and optimization algorithm is presented. A gear-tolerance mathematical model, including profile deviation, pitch deviation, and geometric deviation, was developed by combining traditional profile modeling with a small displacement torsor (SDT) method. Based on this mathematical model, a tooth-contact analysis method, which takes a variety of deviations into account, and a program to compute transmission error were developed. Using the RSM and a genetic algorithm, a gear-tolerance optimization model was created to consider a variety of gear tolerances as design variables and process cost as an optimization objective. An example of gear-tolerance optimization was analyzed, and the result indicates that the method presented in this paper may help improve the efficiency of gear-tolerance optimization and is practicable for precision gear design.

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