A New Method Designed Crimping Technical Parameter Using Response Surface Methodology

2013 ◽  
Vol 423-426 ◽  
pp. 955-961
Author(s):  
Li Feng Fan ◽  
Ying Gao ◽  
Jia Xin Yan ◽  
Jian Bin Yun
Keyword(s):  
Finite Element ◽  
Response Surface Methodology ◽  
Radial Basis Function ◽  
Response Surface ◽  
Basis Function ◽  
Polynomial Function ◽  
New Method ◽  
Surface Model ◽  
Technical Parameters ◽  
Radial Basis

Crimping is widely used in production of large diameter submerged-arc welding pipes. Traditionally, the designers obtain the technical parameters for crimping from experience or trial-errors by experiments. However, it is difficult to obtain the ideal crimping technical parameters with this method immediately at present. To tackle this problem, a new method coupled with response surface methodology and finite element method is proposed to design crimping technical parameters and save the design time of crimping. In this paper, the crimping forming process is simulated by finite element (FE) code ABAQUS. Taking the crimping of X80 steel Φ1219mm×22mm×12000mm welding pipe for instance, the simulation data from the arrangement of simulation which is constituted by the optimal latin hyper-cube sampling approach is treated as sample point. Four types of response surface methodology which included four-order polynomial function, orthogonal polynomial function, kriging and radial basis function is discussed, where the response surface model based on radial basis function is proved more efficient than other types of response surface methodology to construct surrogate model. The results showed a good agreement by a comparison with simulation results and remarkably predicted the crimping quality. Thus, the presented method of this research provides an effective path to design crimping parameters.

scholarly journals Parametric Optimization of the Poly (Nvinylcaprolactam) (PNVCL) Thermoresponsive Polymers Synthesis by the Response Surface Methodology and Radial Basis Function neural network

2018 ◽  
Vol 225 ◽  
pp. 02023
Author(s):  
Marwah N. Mohammed ◽  
Kamal Bin Yusoh ◽  
Jun Haslinda Binti Haji Shariffuddin
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Response Surface Methodology ◽  
Radial Basis Function ◽  
Response Surface ◽  
Basis Function ◽  
Conversion Rate ◽  
Maximum Effect ◽  
Thermoresponsive Polymers ◽  
Radial Basis

A novel comparison study based on a radial basis function neural network (RBFNN) and Response Surface Methodology (RSM) is proposed to predict the conversion rate (yield) of the experimental data for PNVCL polymerization. A statistical and optimization model was performing to show the effect of each parameter and their interactions on the conversion rate. The influence of the time, polymerization temperature, initiator concentration and concentration of the monomer were studied. The results obtained in this study indicate that the RBFNN was an effective method for predicting the conversion rate. The time of the PNVCL polymerization as well as the concentration of the monomer show the maximum effect on the conversion rate. In addition, compared with the RSM method, the RBFNN showed better conversion rate comparing with the experimental data.

Surface roughness modeling using response surface methodology and a variant of multiquadric radial basis function

2020 ◽  
Vol 110 (11-12) ◽  
pp. 3311-3322
Author(s):  
Orquídea Sánchez-López ◽  
Ignacio Hernández-Castillo ◽  
Cuauhtémoc-Héctor Castañeda-Roldán ◽  
Agustin Santiago-Alvarado ◽  
Angel-Sinue Cruz-Félix
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Radial Basis Function ◽  
Response Surface ◽  
Basis Function ◽  
Radial Basis

scholarly journals Upset Prediction in Friction Welding Using Radial Basis Function Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Wei Liu ◽  
Feifan Wang ◽  
Xiawei Yang ◽  
Wenya Li
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Radial Basis Function ◽  
Basis Function ◽  
Welded Joints ◽  
Friction Welding ◽  
Rbf Neural Network ◽  
Finite Element Simulations ◽  
Welding Parameters ◽  
Radial Basis

This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW), a radial basis function (RBF) neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW) and continuous drive friction welding (CDFW). The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.

A Hybrid Method Using Response Surface and Pattern Search for Design Optimization

2005 ◽  
Author(s):  
T. Zhang ◽  
K. K. Choi ◽  
S. Rahman
Keyword(s):  
Radial Basis Function ◽  
Least Squares ◽  
Response Surface ◽  
Basis Function ◽  
Function Approximation ◽  
Least Squares Method ◽  
Order Approximation ◽  
Pattern Search ◽  
Moving Least Squares ◽  
Radial Basis

This paper presents a new method to construct response surface function and a new hybrid optimization method. For the response surface function, the radial basis function is used for a zeroth-order approximation, while new bases is proposed for the moving least squares method for a first-order approximation. For the new hybrid optimization method, the gradient-based algorithm and pattern search algorithm are integrated for robust and efficient optimization process. These methods are based on: (1) multi-point approximations of the objective and constraint functions; (2) a multi-quadric radial basis function for the zeroth-order function representation or radial basis function plus polynomial based moving least squares approximation for the first-order function approximation; and (3) a pattern search algorithm to impose a descent condition. Several numerical examples are presented to illustrate the accuracy and computational efficiency of the proposed method for both function approximation and design optimization. The examples for function approximation indicate that the multi-quadric radial basis function and the proposed radial basis function plus polynomial based moving least squares method can yield accurate estimates of arbitrary multivariate functions. Results also show that the hybrid method developed provides efficient and convergent solutions to both mathematical and structural optimization problems.

Radial basis function surrogate model-based optimization of guardrail post embedment depth in different soil conditions

2019 ◽  
Vol 234 (2-3) ◽  
pp. 739-761
Author(s):  
Sedat Ozcanan ◽  
Ali Osman Atahan
Keyword(s):  
Finite Element ◽  
Radial Basis Function ◽  
Basis Function ◽  
Computational Cost ◽  
Critical Parameters ◽  
Crash Test ◽  
Soil Conditions ◽  
Embedment Depth ◽  
Element Analysis ◽  
Radial Basis

For guardrail designers, it is essential to achieve a crashworthy and optimal system design. One of the most critical parameters for an optimal road restraint system is the post embedment depth or the post-to-soil interaction. This study aims to assess the optimum post embedment depth values of three different guardrail posts embedded in soil with varying density. Posts were subjected to dynamic impact loads in the field while a detailed finite element study was performed to construct accurate models for the post–soil interaction. It is well-known that experimental tests and simulations are costly and time-consuming. Therefore, to reduce the computational cost of optimization, radial basis function–based metamodeling methodology was employed to create surrogate models that were used to replace the expensive three-dimensional finite element models. In order to establish the radial basis function model, samples were derived using the full factorial design. Afterward, radial basis function–based metamodels were generated from the derived data and objective functions performed using finite element analysis. The accuracy of the metamodels were validated by k-fold cross-validation, then optimized using multi-objective genetic algorithm. After optimum embedment depths were obtained, finite element simulations of the results were compared with full-scale crash test results. In comparison with the actual post embedment depths, optimal post embedment depths provided significant economic advantages without compromising safety and crashworthiness. It is concluded that the optimum post embedment depths provide an economic advantage of up to 17.89%, 36.75%, and 43.09% for C, S, and H types of post, respectively, when compared to actual post embedment depths.

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