Welding Parameters

Optimization of different welding processes using statistical and numerical approaches – A reference guide

Abstract
Welding input parameters play a very significant role in determining the quality of a weld joint. The joint quality can be defined in terms of properties such as weld-bead geometry, mechanical properties, and distortion. Generally, all welding processes are used with the aim of obtaining a welded joint with the desired weld-bead parameters, excellent mechanical properties with minimum distortion.
Nowadays, application of design of experiment (DoE), evolutionary algorithms and computational network are widely used to develop a mathematical relationship between the welding process input parameters and the output variables of the weld joint in order to determine the welding input parameters that lead to the desired weld quality. A comprehensive literature review of the application of these methods in the area of welding has been introduced herein. This review was classified according to the output features of the weld, i.e. bead geometry and mechanical properties of the welds.
Article Outline
1. Introduction
2. Weld-bead geometry
2.1. Factorial design
2.2. Linear regression
2.3. Response surface methodology
2.4. Artificial neural networks (ANNs)
2.5. Taguchi method
2.6. Combination of two techniques
2.7. Other techniques
3. Mechanical properties
3.1. Factorial design
3.2. Response surface methodology
3.3. Artificial neural networks
3.4. Taguchi method
3.5. Combination of two techniques
3.6. Other techniques
4. Comparison between the optimization techniques
5. Conclusion
References

Experimental investigation of explosive welding of cp-titanium/AISI 304 stainless steel

Abstract
In explosive welding process, the controlled energy of explosives is used to create a metallurgical bond between two similar or dissimilar materials. This paper presents the analytical calculation for determination of weldability domain or welding window. The analytical calculations are in good agreement with experimental results. The welding conditions are tailored through parallel geometry route with different explosive loads. The study was also conducted to consider the effects of explosive loading on the bonding interface and the characterization of explosive welding experiments carried out under different conditions. Optical microscopy studies show that a transition from a smooth interface to a wavy one occurs with increase in explosive load. Scanning electron microscopy studies show that the interface was outlined by characteristic sharp transition between two materials, but local melted zones were also encountered in the front slope of waves in the interface at high explosive loads. XRD studies detected no intermetallic phases for specimen welded at low explosive load.
Article Outline
1. Introduction
2. Analyses
2.1. The smooth–wavy transition criteria
2.2. Weldability window
3. Experiments
3.1. Experimental procedures
3.1.1. Materials and explosive joining
3.1.2. Microstructure work
3.2. Results and discussion
3.2.1. Metallographic studies
3.2.2. SEM studies
3.2.3. XRD studies
4. Conclusions
References

The effect of process parameters on penetration in gas metal arc welding processes

Abstract
In this study, the effects of various welding parameters on welding penetration in Erdemir 6842 steel having 2.5 mm thickness welded by robotic gas metal arc welding were investigated. The welding current, arc voltage and welding speed were chosen as variable parameters. The depths of penetration were measured for each specimen after the welding operations and the effects of these parameters on penetration were researched.
The welding currents were chosen as 95, 105, 115 A, arc voltages were chosen as 22, 24, and 26 V and the welding speeds were chosen as 40, 60 and 80 cm/min for all experiments. As a result of this study, it was obvious that increasing welding current increased the depth of penetration. In addition, arc voltage is another parameter in incrimination of penetration. However, its effect is not as much as current’s. The highest penetration was observed in 60 cm/min welding current.
Article Outline
1. Introduction
2. Experimental
3. Results and discussion
3.1. The effect of welding current on penetration
3.2. The effect of arc voltage on penetration
3.3. The effect of welding speed on penetration
3.4. Macrostructure
4. Conclusion
Acknowledgements
References

Prediction of the optimized welding parameters for the joined brass plates using genetic algorithm

Abstract
Welding is a major bonding technique in the industry. The importance of welding directed many researches to search how well welding can be obtained. Three main indicators, such as welding current (A), welding velocity (v) and arc length (b), have a big influence in the quality welding. Since all these factors affect the quality of the welded joining parts, the effect of these parameters was investigated experimentally. The present paper describes the use of stochastic search process that is the basis of genetic algorithms (GAs), in developing estimation of the welding parameters for the joined brass plates. Developed models having non-linear estimation models using GA techniques are validated with actual data. Genetic Algorithm Welding Current Estimation Model and Genetic Algorithm Welding Velocity Estimation Model are used to estimate the welding current and velocity according to the welding environment for the brass material.
Article Outline
1. Introduction
2. Genetic algorithms
2.1. Selection
2.2. Crossover
2.3. Mutation
2.4. The fitness function
3. Application of the GAWCEM/GAWVEM model and results
4. Conclusions
References

A Numerical Model for Cold Welding of Metals

Abstract
Based on experimental investigations of cold welding of different metal combinations applying various surface preparation methods, the understanding of the mechanisms of bond formation in cold welding has been improved by introducing two parameters representing the properties of surface layers at the weld interface. Accordingly, the general model for bond strength in cold welding earlier developed by Bay has been extended and modified. The new model presented in this paper simulates the whole cold welding process including the deformation of base metals and the establishment of welds bonding similar as well as dissimilar metals The calculated bond strengths are verified by comparing with experimental measurements.

An investigation into an intelligent system for predicting bead geometry in GMA welding process

|Abstract |
|Gas metal arc (GMA) welding process has been chosen as a metal joining technique due to the wide range of usable applications, cheap|
|consumables and easy handling. The welding quality is generally controlled by the welding parameters. To achieve a high level of |
|welding performance and quality, a suitable algorithm is required to fully understand the influence of welding parameters on bead |
|geometry in the GMA welding process. |
|In this paper, we develop an intelligent system in GMA welding processes using MATLAB/SIMULINK software. Based on multiple |
|regressions and a neural network, the mathematical models are derived from extensive experiments with different welding parameters |
|and complex geometrical features. Graphic displays represent the resulting solution on the bead geometry that can be employed to |
|further probe the model. The developed system enables to input the desired weld dimensions and select the optimal welding |
|parameters. The experimental results were proved the capability of the developed system to select the welding parameters in GMA |
|welding process according to complex external and internal geometrical features of the substrate. |
|Article Outline |
|1. Introduction |
|2. Experimental procedure |
|3. Model development |
|3.1. Multiple regression models |
|3.2. Neural network model |
|4. Implementations and validation for an intelligent system |
|5. Results and discussion |
|6. Conclusion |
|Acknowledgements |
|References |

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