scholarly journals Multivariate Quadratic Nonlinear Regression Model of the Ultimate Pull-Out Load of Electrohydraulic Expansion Joints Based on Response Surface Methodology

Coatings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 689
Author(s):  
Da Cai ◽  
Chenyu Jin ◽  
Jie Liang ◽  
Guangyao Li ◽  
Junjia Cui
Keyword(s):  
Response Surface Methodology ◽  
Regression Model ◽  
Response Surface ◽  
Process Parameters ◽  
Nonlinear Regression ◽  
Discharge Voltage ◽  
Wire Diameter ◽  
Nonlinear Regression Model ◽  
Wire Length ◽  
Pull Out

Electrohydraulic expansion joining has great potential for joining the light weight and high strength thin-walled pipes due to its high strain rate. Based on the central composite design (CCD) of response surface methodology, multiple experiments of electrohydraulic expansion joining process were performed. The multivariate quadratic nonlinear regression model between process parameters (discharge voltage, wire length, and wire diameter) and the ultimate pull-out load of the joints was established. The results revealed that discharge voltage, wire length and wire diameter all had a significant effect on the ultimate pull-out load. The discharge voltage had the most significant effect. The interaction between the discharge voltage and the wire diameter had a significant effect on the ultimate pull-out load. The optimal parameter combination (discharge voltage = 6 kV, wire length = 10 mm, wire diameter = 0.833 mm) was obtained and verified through the experiments. This study would provide guidance for the choice of the process parameters in real applications.

Optimization and Analysis of WEDM Process Parameters Based on Nonlinear Regression Model

2011 ◽  
Vol 411 ◽  
pp. 331-334 ◽  
Author(s):  
Huai Bo Qiang ◽  
Qiong Wu
Keyword(s):  
Regression Model ◽  
Process Parameters ◽  
Nonlinear Regression ◽  
Quality Index ◽  
Electrical Discharge Machining ◽  
Test Method ◽  
Nonlinear Regression Model ◽  
Multiple Parameters ◽  
Machining Quality ◽  
Wedm Process

The process parameters of wire cut electrical discharge machining were optimized to improve machining quality and efficiency.The nonlinear regression model of process parameters and processing index was established. The objective function is presented by test method and an optimization result containing multiple parameters was achieved. The results show that the nonlinear regression model can accurately reflect the relation between process parameters and quality index.

scholarly journals Dimension Analysis-Based Model for Prediction of Shale Compressive Strength

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiangyu Fan ◽  
Fenglin Xu ◽  
Lin Chen ◽  
Qiao Chen ◽  
Zhiwei Liu ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Regression Model ◽  
Nonlinear Regression ◽  
Prediction Error ◽  
Diameter Ratio ◽  
Nonlinear Regression Model ◽  
Average Prediction Error ◽  
Model Average ◽  
Dimension Analysis ◽  
Longmaxi Shale

The compressive strength of shale is a comprehensive index for evaluating the shale strength, which is linked to shale well borehole stability. Based on correlation analysis between factors (confining stress, height/diameter ratio, bedding angle, and porosity) and shale compressive strength (Longmaxi Shale in Sichuan Basin, China), we develop a dimension analysis-based model for prediction of shale compressive strength. A nonlinear-regression model is used for comparison. A multitraining method is used to achieve reliability of model prediction. The results show that, compared to a multi-nonlinear-regression model (average prediction error = 19.5%), the average prediction error of the dimension analysis-based model is 19.2%. More importantly, our dimension analysis-based model needs to determine only one parameter, whereas the multi-nonlinear-regression model needs to determine five. In addition, sensitivity analysis shows that height/diameter ratio has greater sensitivity to compressive strength than other factors.

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