scholarly journals Prediction and optimization of weld bead geometry in gas metal arc welding process using RSM and fmincon

2013 ◽  
Vol 5 (8) ◽  
pp. 154-165 ◽  
Author(s):  
P. Sreeraj
Keyword(s):  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Weld Bead ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Metal Arc Welding ◽  
Arc Welding Process

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.

Dynamic Modeling and Adaptive Control of the Gas Metal Arc Welding Process

1994 ◽  
Vol 116 (3) ◽  
pp. 405-413 ◽  
Author(s):  
Jae-Bok Song ◽  
David E. Hardt
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Weld Bead ◽  
Adaptive Control System ◽  
Bead Width ◽  
Metal Arc Welding ◽  
Arc Welding Process

Control of the welding process is a very important step in welding automation. Since the welding process is complex and highly nonlinear, it is very difficult to accurately model the process for real-time control. In this research, a discrete-time transfer function matrix model for gas metal arc welding process is proposed. This empirical model takes the common dynamics for each output and inherent process and measurement delays into account. Although this linearized model is valid only around the operating point of interest, the adaptation mechanism employed in the control system render this model useful over a wide operating range. Since welding is inherently a nonlinear and multi-input, multi-output process, a multivariable adaptive control system is used for high performance. The process outputs considered are weld bead width and depth, and the process inputs are chosen as the travel speed of the torch and the heat input. A one-step-ahead (or deadbeat) adaptive control algorithm combined with a recursive least-squares methods for on-line parameter estimation is implemented in order to achieve the desired weld bead geometries. Control weighting factors are used to maintain the stability and reduce excessive control effort. Some guidelines for the control design are also suggested. Command following and disturbance rejection properties of the adaptive control system for both SISO and MIMO cases are investigated by simulation and experiment. Although a truly independent control of the outputs is difficult to implement because of a strong output coupling inherent in the process, a control system for simultaneous control of bead width and depth was successfully implemented.

Three-Dimensional Modeling of Gas Metal Arc Welding of Aluminum Alloys

2010 ◽  
Vol 132 (2) ◽  
Author(s):  
H. Guo ◽  
J. Hu ◽  
H. L. Tsai
Keyword(s):  
Weld Pool ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Three Dimensional ◽  
Welding Process ◽  
Weld Bead ◽  
Three Dimensional Modeling ◽  
Dimensional Modeling ◽  
Metal Arc Welding ◽  
Arc Welding Process

A three-dimensional mathematical model and numerical techniques were developed for simulating a moving gas metal arc welding process. The model is used to calculate the transient distributions of temperature and velocity in the weld pool and the dynamic shape of the weld pool for aluminum alloy 6005-T4. Corresponding experiments were conducted and in good agreement with modeling predictions. The existence of a commonly observed cold-weld at the beginning of the weld, ripples at the surface of the weld bead, and crater at the end of the weld were all predicted. The measured microhardness around the weld bead was consistent with the predicted peak temperature and other metallurgical characterizations in the heat-affected zone.

Effect Of Welding Speed On Weld Bead Geometry And Percentage Dilution In Gas Metal Arc Welding Of SS409L

2019 ◽  
Vol 18 ◽  
pp. 5032-5039 ◽  
Author(s):  
Sanjay Kumar Gupta ◽  
Shivansh Mehrotra ◽  
Avinash Ravi Raja ◽  
M.Vashista ◽  
M.Z. Khan Yusufzai
Keyword(s):  
Welding Speed ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Weld Bead ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Metal Arc Welding

scholarly journals Two gas metal arc welding process dataset of arc parameters and input parameters

Data in Brief ◽  
2021 ◽  
Vol 35 ◽  
pp. 106790
Author(s):  
Rogfel Thompson Martinez ◽  
Guillermo Alvarez Bestard ◽  
Sadek C. Absi Alfaro
Keyword(s):  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Metal Arc Welding ◽  
Input Parameters ◽  
Arc Welding Process

Optimization of Argon Gas Metal Arc Welding Process Parameters with Regard to Tensile Strength for AISI 310 Stainless Steel Using Taguchi Technique

2022 ◽  
Vol 58 ◽  
pp. 1-10
Author(s):  
Hanmant Virbhadra Shete ◽  
Sanket Dattatraya Gite
Keyword(s):  
Tensile Strength ◽  
Arc Welding ◽  
Welded Joint ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Taguchi Technique ◽  
Argon Gas ◽  
Metal Arc Welding ◽  
310 Stainless Steel ◽  
Arc Welding Process

Gas metal arc welding (GMAW) is the leading process in the development of arc welding process for higher productivity and quality. In this study, the effect of process parameters of argon gas welding on the strength of T type welded joint of AISI 310 stainless steel is analyzed. The Taguchi technique is used to develop the experimental matrix and tensile strength of the welded joint is measured using experimental method and finite element method. Optimization of input parameter is performed for the maximum tensile strength of welded joint using ANOVA. The results showed that welding speed is the most significant factor affecting the tensile strength followed by voltage in argon gas metal arc welding (AGMAW) process. Argon gas welding process performance with regard to the tensile strength is optimized at voltage: 18.5 V, wire feed speed: 63 m/min and welding speed: 0.36 m/min.

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