Effect of welding parameters on automated robotic arc welding process

2020 ◽  
Vol 26 ◽  
pp. 2363-2367
Author(s):  
Aman Sharma ◽  
Kamal Sharma ◽  
Anas Islam ◽  
Debanik Roy
Keyword(s):  
Arc Welding ◽  
Welding Process ◽  
Welding Parameters ◽  
Robotic Arc Welding ◽  
Arc Welding Process

Development of Exothermic Flux for Enhanced Penetration in Submerged Arc Welding

2020 ◽  
Vol 19 (01) ◽  
pp. 131-146
Author(s):  
Aditya Kumar ◽  
Kulwant Singh
Keyword(s):  
Mathematical Model ◽  
Arc Welding ◽  
Welding Process ◽  
Submerged Arc Welding ◽  
Graphical Form ◽  
Welding Parameters ◽  
Weld Penetration ◽  
Agglomeration Process ◽  
Arc Welding Process ◽  
Submerged Arc

An exothermic flux for submerged arc welding process has been developed which is capable of enhancing weld penetration of the joint. For this purpose, thermit mixture in different proportions (20% and 40%) has been added to the parent flux by agglomeration process. Beads on plate were deposited using parent and developed exothermic fluxes for a comparative study. EH14 filler wires in combination with parent and exothermic fluxes were used in this investigation. The effects of welding parameters and exothermic flux on weld penetration were investigated and the results have been presented in this paper. It has been found that the penetration increases from 2.95 to 3.51[Formula: see text]mm with 40% thermit mixture addition to the parent flux. It is further observed that penetration increases with increase in the amount of thermit mixture added. A mathematical model has been developed to predict weld penetration or select suitable welding parameters to obtain the desired penetration. The significance of coefficients was tested using Student’s [Formula: see text]-test and the adequacy of developed model was tested using [Formula: see text]-test. The effects of various parameters on penetration have been presented in graphical form for better understanding.

Computational Optimization of Arc Welding Parameters Using Coupled Genetic Algorithms and Finite Element Method

2013 ◽  
Author(s):  
Mohammad R. Islam ◽  
Jens Rohbrecht ◽  
Arjaan Buijk ◽  
Ehsan Namazi ◽  
Bing Liu ◽  
...  
Keyword(s):  
Finite Element ◽  
Genetic Algorithms ◽  
Arc Welding ◽  
Welding Process ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Welding Parameters ◽  
Fe Model ◽  
Optimum Parameters ◽  
Arc Welding Process

An effective and rigorous approach to determine optimum welding process parameters is implementation of advanced computer aided engineering (CAE) tool that integrates efficient optimization techniques and numerical welding simulation. In this paper, an automated computational methodology to determine optimum arc welding process control parameters is proposed. It is a coupled Genetic Algorithms (GA) and Finite Element (FE) based optimization method where GA directly utilizes output responses of FE based welding simulations for iterative optimization. Effectiveness of the method has been demonstrated by predicting optimum parameters of a lap joint specimen of two thin steel plates for minimum distortion. Three dimensional FE model has been developed to simulate the arc welding process and validated by experimental results. Subsequently, it is used by GA as the evaluation model for optimization. The optimization results show that such a CAE based method can predict optimum parameters successfully with limited effort and cost.

Control of the weld quality using welding parameters in a robotic welding process

2018 ◽  
Vol 1 (87) ◽  
pp. 32-40 ◽  
Author(s):  
M.-H. Park ◽  
B.-J. Jin ◽  
T.-J. Yun ◽  
J.-S. Son ◽  
C.-G. Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Arc Welding ◽  
Gma Welding ◽  
Welding Process ◽  
Welding Parameters ◽  
Automatic Welding ◽  
Welding Quality ◽  
Arc Welding Process ◽  
Intelligent Models ◽  
Welding System

Purpose: Since the welding automations have widely been required for industries and engineering, the development of the predicted model has become more important for the increased demands for the automatic welding systems where a poor welding quality becomes apparent if the welding parameters are not controlled. The automated welding system must be modelling and controlling the changes in weld characteristics and produced the output that is in some way related to the change being detected as welding quality. To be acceptable a weld quality must be positioned accurately with respect to the joints, have good appearance with sufficient penetration and reduce low porosity and inclusion content. Design/methodology/approach: To achieve the objectives, two intelligent models involving the use of a neural network algorithm in arc welding process with the help of a numerical analysis program MATLAB have been developed. Findings: The results represented that welding quality was fully capable of quantifying and qualifying the welding faults. Research limitations/implications: Welding parameters in the arc welding process should be well established and categorized for development of the automatic welding system. Furthermore, typical characteristics of welding quality are the bead geometry, composition, microstructure and appearance. However, an intelligent algorithm that predicts the optimal bead geometry and accomplishes the desired mechanical properties of the weldment in the robotic GMA (Gas Metal Arc) welding should be required. The developed algorithm should expand a wide range of material thicknesses and be applicable in all welding position for arc welding process. Furthermore, the model must be available in the form of mathematical equations for the automatic welding system. Practical implications: The neural network models which called BP (Back Propagation) and LM (Levenberg-Marquardt) neural networks to predict optimal welding parameters on the required bead reinforcement area in lab joint in the robotic GMA welding process have been developed. Experimental results have been employed to find the optimal algorithm to predict bead reinforcement area by BP and LM neural networks in lab joint in the robotic GMA welding. The developed intelligent models can be estimated the optimal welding parameters on the desired bead reinforcement area and weld criteria, establish guidelines and criteria for the most effective joint design for the robotic arc welding process. Originality/value: In this study, intelligent models, which employed the neural network algorithms, one of AI (Artificial Intelligence) technologies have been developed to study the effects of welding parameters on bead reinforcement area and to predict the optimal bead reinforcement area for lab joint in the robotic GMA welding process. BP (Back Propagation) and LM (Levenberg-Marquardt) neural network algorithm have been used to develop the intelligent model.

Effect of Gas Metal Arc Welding Parameters on Mechanical Properties for Carbon Steel ASTM A285 Grade A

2011 ◽  
Vol 341-342 ◽  
pp. 16-20
Author(s):  
Mongkol Chaisri ◽  
Prachya Peasura
Keyword(s):  
Carbon Dioxide ◽  
Tensile Strength ◽  
Carbon Steel ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Welding Parameters ◽  
Shielding Gas ◽  
Metal Arc Welding ◽  
Arc Welding Process

The research was study the effect of gas metal arc welding process parameters on mechanical property. The specimen was carbon steel ASTM A285 grade A which thickness of 6 mm. The experiments with full factorial design. The factors used in this study are shielding gas and voltage. The welded specimens were tested by tensile strength testing and hardness testing according to ASME boiler and pressure vessel code section IX 2007. The result showed that both of shielding gas and voltage had interaction on tensile strength and hardness at 95% confidential (P value < 0.05). Factors affecting the tensile strength are the most carbon dioxide and 27 voltage were tensile strength 213.43 MPa. And hardness maximum of 170.60 HV can be used carbon dioxide and 24 voltage. This research can be used as data in the following appropriate parameters to gas metal arc welding process.

scholarly journals Hybrid laser arc welding: State of the art in technology

2021 ◽  
Vol 66 (3) ◽  
pp. 115-124
Author(s):  
Martin Petreski ◽  
Dobre Runchev ◽  
Gligorche Vrtanoski
Keyword(s):  
Industrial Application ◽  
Arc Welding ◽  
Stable Process ◽  
Welding Process ◽  
Welding Parameters ◽  
Heat Sources ◽  
Hybrid Laser Arc Welding ◽  
Current State ◽  
Complex Process ◽  
Arc Welding Process

Hybrid laser arc welding is complex process where two heat sources act simultaneously in a common weld pool. The synergy effect of laser beam and electric arc offers several advantages over other individual technological processes, such as: higher welding speed, increased productivity, deeper penetration, better gap bridging ability, stable process, less heat input to the welding material, etc. However, the combination of two heat sources in a single welding process leads to large number of parameters that need to be synchronized and optimized in order to obtain a perfect weld. This paper presents the current state of hybrid laser arc welding in terms of its development, industrial application and scientific research. The introduction part contains a general overview of the hybrid laser arc welding process, its advantages and operating principles, and chronological development. In the second part, welding parameters that directly influence on the hybrid process have been discussed. The third part presents the performance and weld qualities achieved by hybrid welding process in accordance with previous research. In the final part, examples of industrial application and conclusions for further research and development related to hybrid laser arc welding are given.

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