PREDICTION OF TORQUE IN MILLING BY RESPONSE SURFACE METHOD AND NEURAL NETWORK

2009 ◽  
Vol 29 (1) ◽  
Author(s):  
K. Kadirgama∗ ◽  
K.A. Abou-El-Hossein∗∗
Keyword(s):  
Neural Network ◽  
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.

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.

Structural Reliability Analysis Using Fourier Orthogonal Neural Network Method

2013 ◽  
Vol 838-841 ◽  
pp. 360-363 ◽  
Author(s):  
Li Rong Sha ◽  
Yue Yang
Keyword(s):  
Neural Network ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Failure Probability ◽  
Structural Reliability ◽  
Random Variables ◽  
Structural Reliability Analysis ◽  
Surface Method ◽  
Structural Responses

In order to predict the failure probability of a complicated structure, the structural responses usually need to be predicted by a numerical procedure, such as FEM method. The response surface method could be used to reduce the computational effort required for reliability analysis. However the conventional response surface method is still time consuming when the number of random variables is large. In this paper, a Fourier orthogonal neural network (FONN)-based response surface method is proposed. In this method, the relationship between the random variables and structural responses is established using FONN models. Then the FONN model is connected to the first order and second moment method (FORM) to predict the failure probability. Numerical example result shows that the proposed approach is efficient and accurate, and is applicable to structural reliability analysis.

Estimating collapse risk and reliability of concrete moment frame structure using response surface method and hybrid of artificial neural network with particle swarm optimization algorithm

2021 ◽  
pp. 1748006X2110074
Author(s):  
Mohammad Amin Bayari ◽  
Naser Shabakhty ◽  
Esmaeel Izadi Zaman Abadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface ◽  
Response Surface Method ◽  
Fragility Curves ◽  
Pso Algorithm ◽  
Computational Effort ◽  
Structural Collapse ◽  
Surface Method ◽  
Modeling Uncertainties

Structural collapse performance assessment has been at the center of many researchers’ interest due to complications of this phenomenon and uncertainties involved in modeling the simulation of the structural collapse response. This research aims to predict the structural collapse responses including mean collapse capacity, collapse standard deviation, and collapse drift by considering modeling uncertainties and then estimating collapse fragility curves, collapse risk, and reliability using Response Surface Method (RSM) and Artificial Neural Network (ANN). Modeling uncertainties for evaluating collapse responses are the parameters of the modified Ibarra-Krawinkler moment-rotation curve. Moreover, to analyze the structural uncertainty, the correlation between the model parameters in one component and between two structural components was considered. The Latin Hypercube Sampling (LHS) method and Cholesky decomposition were used to produce independent and dependent random variables, respectively. To predict the collapse responses of the structure, taking into account the uncertainties, as the number of uncertainties increases, the number of simulations for the uncertainties also increases, leading to a significant increase in the computational effort to estimate the structural responses, in the presence of a limited number of samples for uncertainties, a hybrid of ANN with PSO algorithm was used to reduce the computational effort in order to estimate the collapse fragility curves, collapse risk, and structural reliability. The results show that structural collapse responses can be predicted with appropriate accuracy by producing a limited number of samples for uncertainties and using an ANN-PSO algorithm.

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