scholarly journals The Effect of Laser Beam Wobbling Mode on Weld Bead Geometry of Tailor Welded Blanks

2020 ◽  
Vol 3 (1) ◽  
pp. 282-290
Author(s):  
Celalettin Yuce
Keyword(s):  
Laser Beam ◽  
Production Efficiency ◽  
The Body ◽  
Weld Bead ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Bead Width ◽  
Body In White ◽  
Welding Processes ◽  
Beam Oscillation

As a higher weight leads to increased fuel consumption for the automobile industry, the body in white must be lighter to compensate for the weight of additional components. Therefore, tailored blanks are used, which reinforce the body in white only in areas where a higher strength or stiffness is necessary. The applicability of laser welding processes with its numerous advantages, such as low heat input and production efficiency, is often limited when joining imperfect edges steel sheets due to small gap bridging ability. To overcome this limit, recent developments in the laser industry have introduced a novel method to wider the applications of lasers through the utilization of fast beam oscillation techniques, also known as laser beam wobbling. In this study, the effects of the four different amplitudes (0.5 mm, 1 mm, 1.5 mm and 2 mm) of circular laser beam oscillation patterns on the weld bead geometry and microhardness distribution were investigated. The results revealed that the weld bead width increased with the increase of wobble amplitude. Moreover, the tensile strengths of the welded blanks were higher than the AHSS base metal for all amplitude levels.

A Model for In-Process Control of Thermal Properties During Welding

1989 ◽  
Vol 111 (1) ◽  
pp. 40-50 ◽  
Author(s):  
C. C. Doumanidis ◽  
D. E. Hardt
Keyword(s):  
Process Control ◽  
Thermal Response ◽  
Weld Bead ◽  
Distributed Parameter ◽  
Bead Geometry ◽  
Actual Process ◽  
Weld Bead Geometry ◽  
Thermally Activated ◽  
Real Time Tracking ◽  
Welding Processes

The control of welding processes has received much attention in the past decade, with most attention placed on real-time tracking of weld seams. The actual process control has been investigated primarily in the context of weld bead geometry regulation, ignoring for the most part the metallurgical properties of the weld. This paper addresses the latter problem through development of a model for in-process control of thermally activated material properties of weld. In particular, a causal model relating accessible inputs to the outputs of weld bead area, heat affected zone width, and centerline cooling rate at a critical temperature is developed. Since the thermal system is a distributed parameter, nonlinear one, it is modelled numerically to provide a baseline of simulation information. Experiments are performed that measure the thermal response of actual weldments and are used to calibrate the simulation and then to verify the basic dynamics predicted. Simulation results are then used to derive a locally linear transfer function matrix relating inputs and outputs. These are shown to be nonstationary, depending strongly upon the operating point and the boundary conditions.

Development of Mathematical Models for Prediction of Weld Bead Geometry of GTAW Stainless Steel

2017 ◽  
Vol 867 ◽  
pp. 88-96
Author(s):  
S.M. Ravikumar ◽  
P. Vijian
Keyword(s):  
Stainless Steel ◽  
Process Parameters ◽  
Welding Current ◽  
Weld Bead ◽  
304 Stainless Steel ◽  
Graphical Form ◽  
Bead Geometry ◽  
Depth Of Penetration ◽  
Weld Bead Geometry ◽  
Bead Width

Welding input process parameters are playing a very significant role in determining the weld bead quality. The quality of the joint can be defined in terms of properties such as weld bead geometry, mechanical properties and distortion. Experiments were conducted to develop models, using a three factor, five level factorial design for 304 stainless steel as base plate with ER 308L filler wire of 1.6 mm diameter. The purpose of this study is to develop the mathematical model and compare the observed output values with predicted output values. Welding current, welding speed and nozzle to plate distance were chosen as input parameters, while depth of penetration, weld bead width, reinforcement and dilution as output parameters. The models developed have been checked for their adequacy. Confirmation experiments were also conducted and the results show that the models developed can predict the bead geometries and dilution with reasonable accuracy. The direct and interaction effect of the process parameters on bead geometry are presented in graphical form.

scholarly journals Experimental and Numerical Analysis on TIG Arc Welding of Stainless Steel Using RSM Approach

Metals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1659
Author(s):  
Sasan Sattarpanah Karganroudi ◽  
Mahmoud Moradi ◽  
Milad Aghaee Attar ◽  
Seyed Alireza Rasouli ◽  
Majid Ghoreishi ◽  
...  
Keyword(s):  
Heat Flux ◽  
Stainless Steel ◽  
Welding Speed ◽  
Arc Welding ◽  
Welding Process ◽  
Weld Bead ◽  
Bead Geometry ◽  
Depth Of Penetration ◽  
Weld Bead Geometry ◽  
Bead Width

This study involves the validating of thermal analysis during TIG Arc welding of 1.4418 steel using finite element analyses (FEA) with experimental approaches. 3D heat transfer simulation of 1.4418 stainless steel TIG arc welding is implemented using ABAQUS software (6.14, ABAQUS Inc., Johnston, RI, USA), based on non-uniform Goldak’s Gaussian heat flux distribution, using additional DFLUX subroutine written in the FORTRAN (Formula Translation). The influences of the arc current and welding speed on the heat flux density, weld bead geometry, and temperature distribution at the transverse direction are analyzed by response surface methodology (RSM). Validating numerical simulation with experimental dimensions of weld bead geometry consists of width and depth of penetration with an average of 10% deviation has been performed. Results reveal that the suggested numerical model would be appropriate for the TIG arc welding process. According to the results, as the welding speed increases, the residence time of arc shortens correspondingly, bead width and depth of penetration decrease subsequently, whilst simultaneously, the current has the reverse effect. Finally, multi-objective optimization of the process is applied by Derringer’s desirability technique to achieve the proper weld. The optimum condition is obtained with 2.7 mm/s scanning speed and 120 A current to achieve full penetration weld with minimum fusion zone (FZ) and heat-affected zone (HAZ) width.

A Study of Software Approach for Predicting Weld Bead Geometry in Shielded Metal Arc Welding (SMAW) Process

2014 ◽  
Vol 554 ◽  
pp. 386-390
Author(s):  
C.W. Mohd Noor ◽  
Manuhutu Ferry ◽  
W.B. Wan Nik
Keyword(s):  
Arc Welding ◽  
Welding Process ◽  
Weld Bead ◽  
Bead Geometry ◽  
Shielded Metal Arc Welding ◽  
Depth Of Penetration ◽  
Weld Bead Geometry ◽  
Bead Width ◽  
Real Process ◽  
Metal Arc Welding

The prediction of the optimal weld bead width is an important aspect in shielded metal arc welding (SMAW) process as it is related to the strength of the weld. This paper focuses on investigation of the development of the simple and accurate model for prediction of weld bead geometry. The experiment used welding current, arc length, welding speed, welding gap and electrode diameter as input parameters. While output parameters are bead width, depth of penetration and weld reinforcement. A number of 33 mild steel plate specimens had undergone the SMAW welding process. The experimental data was used to develop mathematical models using SPSS software. The actual and predicted values of the weld bead geometry are compared. The proposed models shows positive correlation to the real process.

scholarly journals Prediction and Optimization of Weld Bead Geometry

2019 ◽  
Vol 8 (10) ◽  
pp. 2735-2742
Keyword(s):  
Weld Bead ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Bead Width ◽  
Current Voltage ◽  
Weld Parameters ◽  
Curvilinear Regression ◽  
Travel Rate

Bead geometry plays very important role in predicting the quality of weld as cooling rate of the weld depends on the height and bead width, also bead geometry determines it’s residual stresses and distortion. Weld bead geometries are outcomes of several welding parameters taken into consideration. If arc travel is high and arc power is kept low it will produce very low fusion. If electrode feed rate is kept higher width is also found to be on higher side which makes bead tto flat. Also, the parameters like current, voltage, arc travel rate, polarity affects weld bead geometry. Hence, this paper uses techniques like ANN, linear regression and curvilinear regression for predictions of weld bead geometry and their relations with different weld parameters. I. INTRODU

Prediction of Weld Bead Geometry in Chromium-Manganese Stainless Steel Gas Tungsten Arc Welded Plates Using Artificial Neural Networks

2013 ◽  
Vol 3 (3) ◽  
pp. 13-41 ◽  
Author(s):  
R. Sudhakaran ◽  
P. S. Siva Sakthivel
Keyword(s):  
Stainless Steel ◽  
Process Parameters ◽  
Weld Bead ◽  
Fractional Factorial ◽  
Welding Parameters ◽  
Width Ratio ◽  
Bead Geometry ◽  
Depth Of Penetration ◽  
Weld Bead Geometry ◽  
Bead Width

The quality of the weld joint is highly influenced by the welding parameters. Hence accurate prediction of weld bead parameters is highly essential to achieve good quality joint. This paper presents development of neural network models for predicting bead parameters such as depth of penetration, bead width and depth to width ratio for AISI 202 grade stainless steel GTAW plates. The use of this series in certain applications ended in failure of the product as there is no adequate level of user knowledge. Hence it becomes imperative to go for detailed investigations on this grade before recommending it for any application. The process parameters chosen for the study are welding current, welding speed, gas flow rate and welding gun angle. The chosen output parameters were depth of penetration, bead width and depth to width ratio. The experiments were conducted based on design of experiments using fractional factorial with 125 runs. Using the experimental data feed forward back propagation neural net work models were developed and trained using Levenberg Marquardt algorithm. The training, learning, performance and transfer functions used are trainlm, learningdm, MSE and tansig respectively. Four networks were developed with four neurons for the input layer, 3 neurons for the output layer and different nodes for the hidden layer. They are 4 – 2 – 3, 4 – 4 – 3, 4 – 8 – 3 and 4 – 9 – 3. It was found that ANN model based on network 4 – 9 – 3 predicted the bead dimensions more accurately than the other networks. The prediction of weld bead geometry parameters helps in identifying the recommended combination of process parameters to achieve good quality joint.

Parametric Optimization of TIG Welding on 316L Austenitic Stainless Steel by Grey-Based Taguchi Method

2011 ◽  
Vol 383-390 ◽  
pp. 4667-4671 ◽  
Author(s):  
Nanda Naik Korra ◽  
K.R. Balasubramanian
Keyword(s):  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Gas Flow ◽  
Welding Process ◽  
Weld Bead ◽  
Bead Geometry ◽  
Depth Of Penetration ◽  
Weld Bead Geometry ◽  
Bead Width ◽  
Input Parameters

Gas Tungsten Arc Welding (GTAW) is one of the most widely used welding process in industry. The input parameters play a very significant role in determining the quality of a welded joint (geometry of weld bead). The joint quality can be evaluated by studying the features of weld bead geometry (output parameters) such as Bead Width (BW), Bead Height (BH) and Depth of Penetration (DP). Present study focused on welding of austenitic stainless steel sheets using GTAW process with 316L material. The output variables are determined according to gas flow rate, travel speed and current. Grey relational analysis is applied to optimize the input parameters simultaneously considering the multiple output variables. Finally, confirmation experiment has been conducted to validate the optimized parameters and found to be correlated.

Modeling of weld bead geometry for rapid manufacturing by robotic GMAW

2015 ◽  
Vol 29 (10n11) ◽  
pp. 1540033 ◽  
Author(s):  
Tao Yang ◽  
Jun Xiong ◽  
Hui Chen ◽  
Yong Chen
Keyword(s):  
Gas Metal Arc Welding ◽  
Dimensional Accuracy ◽  
Great Accuracy ◽  
Weld Bead ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Bead Width ◽  
Metal Arc Welding ◽  
Bead Height ◽  
Metallic Parts

Weld-based rapid prototyping (RP) has shown great promises for fabricating 3D complex parts. During the layered deposition of forming metallic parts with robotic gas metal arc welding, the geometry of a single weld bead has an important influence on surface finish quality, layer thickness and dimensional accuracy of the deposited layer. In order to obtain accurate, predictable and controllable bead geometry, it is essential to understand the relationships between the process variables with the bead geometry (bead width, bead height and ratio of bead width to bead height). This paper highlights an experimental study carried out to develop mathematical models to predict deposited bead geometry through the quadratic general rotary unitized design. The adequacy and significance of the models were verified via the analysis of variance. Complicated cause–effect relationships between the process parameters and the bead geometry were revealed. Results show that the developed models can be applied to predict the desired bead geometry with great accuracy in layered deposition with accordance to the slicing process of RP.

scholarly journals Different Methods for Predicting and Optimizing Weld Bead Geometry with Mathematical Modeling and ANN Technique

2019 ◽  
Vol 8 (10) ◽  
pp. 2761-2766
Keyword(s):  
Weld Bead ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Bead Width ◽  
Current Voltage ◽  
Modeling Techniques ◽  
Weld Parameters ◽  
Travel Rate

Bead geometry plays very important role in predicting the quality of weld as cooling rate of the weld depends on the height and bead width, also bead geometry determines it’s residual stresses and distortion. Weld bead geometries are outcomes of several welding parameters taken into consideration. If arc travel is high and arc power is kept low it will produce very low fusion. If electrode feed rate is kept higher width is also found to be on higher side which makes bead tto flat. Also, the parameters like current, voltage, arc travel rate, polarity affects weld bead geometry. Hence, this paper is a review of different experimentations and modeling techniques regarding predictions of weld bead geometry and their relations with different weld parameters.

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