An investigation into welding parameters affecting the tensile properties of titanium welds

Yung, Kam Chuen; Ralph, B.; Lee, W.B.; Fenn, R.

doi:10.1016/s0924-0136(96)02719-7

Cited by 45 publications

(15 citation statements)

References 6 publications

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“…Cooling rate has an extremely important effect on the mechanical properties of α + β alloys. It was stressed that toughness decreases are experienced at fast cooling conditions, due to the formation of finer structured α platelets, while at slower cooling rates, toughness is improved by diverting crack propagation paths with the coarsening of α platelets [27].…”

Section: Charpy V-notch Impact Testmentioning

confidence: 99%

Robotic Nd:YAG Fiber Laser Welding of Ti-6Al-4V Alloy

Köse

Karaca

2017

Metals

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Abstract:In the present study, Ti6Al4V titanium alloy plates were joined using a robotic fiber laser welding method. The laser welding process was carried out at two different welding speeds. Effects of different heat input conditions on the microstructure and mechanical properties of robotic fiber laser welded joints were investigated. Some grain coarsening was observed in the microstructure of weld metal in samples joined using high heat input, compared to those using low heat input, and volume rates of primary α structures increased in the weld metal. The microstructure of weld metal in samples joined using low heat input was made of basket-weave or acicular α' grains and primary β grains in grain boundaries. Tensile and yield strength of samples joined using low heat input were higher than for those joined using high heat input, but their ductility was lower.

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Section: Charpy V-notch Impact Testmentioning

confidence: 99%

Robotic Nd:YAG Fiber Laser Welding of Ti-6Al-4V Alloy

Köse

Karaca

2017

Metals

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“…However, during the second batch of welding, repeatability could not be achieved and resulted in lot of rejection due to lack of fusion (LF), porosity, top bead sinking, etc. This was due to the fact that the fixed parameters were so sensitive that even a marginal reduction in heat input caused LF, where as a marginal increase in beam power resulted in top bead undercut [10,11].…”

Section: Single Pass Partial Penetration Welding Of 21 MM Thick Jointmentioning

confidence: 99%

Investigations into the effects of electron beam welding on thick Ti–6Al–4V titanium alloy

Saresh

Pillai²,

Mathew

2007

Journal of Materials Processing Technology

159

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“…It also uses mathematical investigation to predict the response at any point within the experimental limits. In welding research, the aim of these methods is the optimization of the different parameters used in welding [2], for example, Parikshit [3], who carried out modeling of a tungsten inert gas (TIG) welding process applying conventional regression analysis and neural network-based approaches and found that the neural network approach is much better than conventional regression analysis. In the paper, it was claimed that the neural network-based approach is better than conventional regression methods since the neural network-based approach can carry out interpolation within a certain range.…”

Section: Introductionmentioning

confidence: 99%

Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep Learning

Kesse

Buah

Handroos

et al. 2020

Metals

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Recent developments in artificial intelligence (AI) modeling tools allows for envisaging that AI will remove elements of human mechanical effort from welding operations. This paper contributes to this development by proposing an AI tungsten inert gas (TIG) welding algorithm that can assist human welders to select desirable end factors to achieve good weld quality in the welding process. To demonstrate its feasibility, the proposed model has been tested with data from 27 experiments using current, arc length and welding speed as control parameters to predict weld bead width. A fuzzy deep neural network, which is a combination of fuzzy logic and deep neural network approaches, is applied in the algorithm. Simulations were carried out on an experimental test dataset with the AI TIG welding algorithm. The results showed 92.59% predictive accuracy (25 out of 27 correct answers) as compared to the results from the experiment. The performance of the algorithm at this nascent stage demonstrates the feasibility of the proposed method. This performance shows that in future work, if its predictive accuracy is improved with human input and more data, it could achieve the level of accuracy that could support the human welder in the field to enhance efficiency in the welding process. The findings are useful for industries that are in the welding trade and serve as an educational tool.

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An investigation into welding parameters affecting the tensile properties of titanium welds

Cited by 45 publications

References 6 publications

Robotic Nd:YAG Fiber Laser Welding of Ti-6Al-4V Alloy

Robotic Nd:YAG Fiber Laser Welding of Ti-6Al-4V Alloy

Investigations into the effects of electron beam welding on thick Ti–6Al–4V titanium alloy

Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep Learning

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