Modeling of Resistance Spot Welding Process Using Nonlinear Regression Analysis and Neural Network Approach on Galvanized Steel Sheet

2011 ◽  
Vol 291-294 ◽  
pp. 823-828
Author(s):  
Yi Luo ◽  
Chun Tian Li ◽  
Hui Bin Xu
Keyword(s):  
Spot Welding ◽  
Steel Sheet ◽  
Back Propagation ◽  
Welding Process ◽  
Galvanized Steel ◽  
Welding Quality ◽  
Back Propagation Algorithm ◽  
Neural Network Approach ◽  
Newton Raphson ◽  
Current Duration

Modeling of resistance spot welding process on galvanized steel sheet was investigated. Mathematical models developed by nonlinear multiple regression analysis and artificial neural network approach were employed in the prediction of welding quality factors, namely nugget diameter, penetration rate and tensile shear strength, under some welding conditions. According to the prediction models on quality, the prediction systems of welding process parameters were formulated respectively on the basis of Newton-Raphson iterative algorithm and cascade forward back propagation algorithm in order to obtain the desired welding quality. The results showed that the prediction precision of cascade forward back propagation algorithm was higher than Newton-Raphson iterative algorithm. The current duration had the largest prediction error, followed by electrode force and welding current. Therefore, it was concluded that the current duration was the most difficult parameter in prediction system of welding process in order to obtain the desired welding quality.

Resistance Spot Welding Process of Galvanized Steel Sheet Based on Regression Modeling

2009 ◽  
Vol 610-613 ◽  
pp. 681-686
Author(s):  
Yi Luo ◽  
Hong Ye ◽  
Cheng Zhi Xiong ◽  
Lin Liu ◽  
Xu Wei Lv
Keyword(s):  
Resistance Spot Welding ◽  
Spot Welding ◽  
Steel Sheet ◽  
Welding Process ◽  
Welding Current ◽  
The Body ◽  
Galvanized Steel ◽  
Spot Welds ◽  
Current Electrode ◽  
Resistance Spot

The resistance spot welding process of galvanized steel sheet used in the body manufacturing of family car was studied, and the indexes of nugget geometry and tensile-shear strength of spot welds were tested. Four process parameters, namely welding current, electrode force, welding current duration and preheat current, and interactions among them were regarded as factors impacting indexes. Method using in mathematical models developing was nonlinear multiple orthogonal regression assembling design, which was optimized by the technology of variance analysis. The experimental results showed that more accurate prediction on nugget size and mechanical properties of spot welds can be obtained by the models optimized. With these prediction results, the optimization of welding process also was realized by the analysis to effect of the parameters and interactions on the welding quality.

Strengthening Mechanism of TiB2-TiC Complex Phases Coated Electrode

2012 ◽  
Vol 433-440 ◽  
pp. 251-255 ◽  
Author(s):  
Ping Luo ◽  
Shi Jie Dong ◽  
Zhang Qiang Mei ◽  
Zhi Xiong Xie
Keyword(s):  
Failure Mechanism ◽  
Current Density ◽  
Spot Welding ◽  
Single Phase ◽  
Welding Process ◽  
Strengthening Mechanism ◽  
Galvanized Steel ◽  
Steel Weld ◽  
Galvanized Steels ◽  
Coated Electrode

TiB2-TiC complex phases coating deposited onto the surface of electrodes by electro-spark deposition (ESD) in order to prolong the life of single phase coated electrode (TiB2 or TiC) during resistance welding of galvanized steels. The microstructures and TiB2-TiC complex phases coatings were characterized by SEM and XRD. The results indicate that life of TiB2-TiC complex phases coated electrode is prolonged significantly than life of single-phase coated electrode （TiB2 or TiC ）, failure mechanism of TiB2-TiC complex phases coated electrode is mainly wear to cause diameter increase on electrode tip, which results in lower current density during welding process, and then nugget size cannot satisfy the requirement of resistance spot welding. The failure mechanism of TiB2-TiC complex phases coated electrode is obviously different from uncoated electrode, the failure mechanism of uncoated electrode is wear and alloying between electrode tip surface and molten Zn on galvanized steel weld surface.

scholarly journals Numerical Simulation Mode of Spot Contact Welding Processes

2021 ◽  
pp. 85-91
Author(s):  
А.С. Угловский ◽  
И.М. Соцкая ◽  
Е.В. Шешунова
Keyword(s):  
Spot Welding ◽  
Low Carbon Steel ◽  
Welding Process ◽  
Welding Parameters ◽  
Low Carbon ◽  
Detailed Data ◽  
Welding Quality ◽  
Welding Processes ◽  
Contact Welding

Цель рассмотрения численного метода заключалась в получении подробных данных, позволяющих оценить проведение сварочного процесса: изменение объёма сварного шва, радиуса сварного шва, радиуса зоны термического влияния. При проведении моделирования авторами выведены зависимости параметров точечной сварки низкоуглеродистой стали толщиной до 3,2 мм. Данные зависимости будут определять качество сварных швов. Соответствующее сочетание параметров точечной сварки обеспечит прочное соединение и хорошее качество сварки. The purpose of the numerical method consideration was to obtain detailed data allowing evaluating the performance of the welding process: changing the volume of the weld, the radius of the weld, the radius of the weld-affected zone. During the simulation the authors have derived dependencies of the parameters of spot welding of low-carbon steel up to 3.2 mm thick. These dependencies will determine the quality of the welds. The correct combination of spot welding parameters will ensure a firm joint and good welding quality.

Research on Microstructure and Wear Resistance of Double Glow Plasma Discharge Surface Titanizing on CrZrCu Alloy

2010 ◽  
Vol 154-155 ◽  
pp. 79-82
Author(s):  
Bo Lin He ◽  
Fang Tu ◽  
Jing Liu
Keyword(s):  
Wear Resistance ◽  
Spot Welding ◽  
Steel Sheet ◽  
Mechanical Performance ◽  
Layer Structure ◽  
Plasma Discharge ◽  
Experimental Results ◽  
Galvanized Steel ◽  
Glow Discharge Plasma ◽  
Glow Plasma

CrZrCu is extensively used in spot welding due to their high electrical, thermal and mechanical performance. But when CrZrCu electrode is used in spot welding of galvanized steel sheet, the abrasion, corrosion and oxidation shorten the electrode life rapidly. The electrode is not able to meet the needs of spot welding of galvanized steel sheet .In the paper, double glow plasma discharge surface titanizing was carried out on CrZrCu alloy. The processes of double glow plasma discharge titanizing, surface alloying layer structure were also analyzed elementary. The wear resistance property of glow discharge plasma titanizing layer was also researched. The experimental results indicated that in the plasma titanizing alloying layer, the diffusion of titanium element toward the interior of the CrZrCu alloy substrate forms the intermetallic compound named Cu4Ti, which is beneficial to the reinforcement of CrZrCu substrate. The experimental results confirmed that double glow plasma discharge titanizing could improve the wear resistance of CrZrCu alloy greatly. Under the external load of 300N and 500N, the wear resistance of double glow plasma discharge titanizing layer is7 times and 8.5 times than that of CrZrCu.

Spot Welding Quality Prediction Model Based on Partial Least-Squares Regression

2013 ◽  
Vol 740 ◽  
pp. 223-225
Author(s):  
Hui Zhao ◽  
Jian Jun Wu ◽  
An Du ◽  
Rui Na Ma ◽  
Yong Zhe Fan
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Spot Welding ◽  
Partial Least Squares Regression ◽  
Welding Process ◽  
Quality Prediction ◽  
Least Squares Regression ◽  
Welding Quality ◽  
Nugget Diameter ◽  
The Relationship

The relationship between spot welding process parameters and nugget diameter was selected and synthesized by partial least squares. The partial least squares regression model of spot welding quality was established. And the nugget diameter was predicted based on the model mentioned above. The results show that this model can be used to predict the spot welding quality.

scholarly journals Artificial Neural Network Approach for Solving Fuzzy Fractional order initial value problems under H-differentiability

2020 ◽  
Author(s):  
Somayeh Ezadi ◽  
Tofigh Allahviranloo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Initial Conditions ◽  
Error Function ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Neural Network Approach ◽  
Error Back Propagation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper aims to solve the celebrated Fuzzy Fractional Differential Equations (FFDE) using an Artificial Neural Network (ANN) technique. Compared to the integer order differential equation, the proposed FFDE can better describe several real application problems of various physical systems. To accomplish the aforementioned aim, the error back propagation algorithm and a multi-layer feed forward neural architecture are utilized using the unsupervised learning in order to minimize the error function as well as the modification of the parameters such as weights and biases. By combining the initial conditions with the ANN, output provides an appropriate approximate solution of the proposed FFDE. Then, two illustrative examples are solved to confirm the applicability of the concept as well as to demonstrate both the precision and effectiveness of the developed method. By comparing with some traditional methods, the obtained results reveals a close match that confirms both accuracy and correctness of the proposed method.

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