Effects of Beam Oscillation on Porosity and Intermetallic Compounds Formation of Electron Beam Welded DP600 Steel to Al-5754 Alloy Joints

Dinda, Soumitra Kumar; Srirangam, Prakash; Roy, Gour Gopal

doi:10.1007/978-3-030-05861-6_21

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…This is so, because with the increase in E, liquid melt-pool gets more time and energy, which will help to create more porosity eventually, but due to the prolong time and more opening time of melt-pool, coalescence of small pores to form the bigger one is also carried out. Similar observations on the change in porosity attributes as a function of heat input, role of cooling rate on the restriction of gas movement out of the melt zone etc., have been reported in the literature [19,20,59]. It is interesting to note that as E is increased further from 0.487 kJ/mm to 0.526 kJ/mm, is decreased from 19 to 15.…”

Section: E Vs Different Micro-porosity Featuresmentioning

confidence: 56%

Study of Micro-porosity in Electron Beam Butt Welding

Das¹,

Dinda²,

Das³

et al. 2021

Preprint

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As complete elimination of porosity from the weld is very difficult, the next option available is to minimize this weld porosity, which is crucial for the safe performance of the welded components. However, this investigation through experiments alone is very tedious and time consuming. Additionally, very limited models are available in the literature for accurate prediction of different porosity attributes. The present study, thus, addressees both the experimental as well as modelling aspect on the study of micro-porosity during Electron Beam Welding (EBW) of SS304 plates. Welding parameters are reported to have significant influence on the micro-porosity. Hence, the influences of these parameters on micro-porosity attributes, namely pores number, average diameter, and sphericity are extensively studied experimentally employing optical microscopy (OM), scanning electron microscopy (SEM), X-ray computed tomography (XCT), and Raman spectroscopy. This is followed by an elaborate modelling through seven popular and well-recognized Machine Learning Algorithms (MLAs) namely, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), M5P model trees regression, Reduced Error Pruning Tree (REPTree), Random Forest (RF), Instance-based k-nearest neighbor algorithm (IBk) and Locally Weighted Learning (LWL). These different techniques enhance the chance of obtaining the better predictions of the said micro-porosity attributes by overcoming the effect of data-dependence and other limitations of individual MLAs. The different model-predicted micro-porosity data are also validated through experimental data. Statistical tests and Monte-Carlo reliability analysis are additionally utilized to evaluate the performances of the employed algorithms. IBk and MLP are overall found to perform well.

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Section: E Vs Different Micro-porosity Featuresmentioning

confidence: 56%

Study of Micro-porosity in Electron Beam Butt Welding

Das¹,

Dinda²,

Das³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Section: E Vs Different Micro-porosity Featuresmentioning

confidence: 59%

Study of micro-porosity in electron beam butt welding

Das

Dinda

Das

et al. 2022

Int J Adv Manuf Technol

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As complete elimination of porosity from the weld is very difficult, the next option available is to minimize this weld porosity, which is crucial for the safe performance of the welded components. However, this investigation through experiments alone is very tedious and time consuming. Additionally, very limited models are available in the literature for accurate prediction of different porosity attributes. The present study, thus, addressees both the experimental as well as modelling aspect on the study of micro-porosity during Electron Beam Welding (EBW) of SS304 plates. Welding parameters are reported to have significant influence on the micro-porosity. Hence, the influences of these parameters on micro-porosity attributes, namely pores number, average diameter, and sphericity are extensively studied experimentally employing optical microscopy (OM), scanning electron microscopy (SEM), X-ray computed tomography (XCT), and Raman spectroscopy. This is followed by an elaborate modelling through seven popular and wellrecognized Machine Learning Algorithms (MLAs) namely, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), M5P model trees regression, Reduced Error Pruning Tree (REPTree), Random Forest (RF), Instance-based k-nearest neighbor algorithm (IBk) and Locally Weighted Learning (LWL). These different techniques enhance the chance of obtaining the better predictions of the said micro-porosity attributes by overcoming the effect of data-dependence and other limitations of individual MLAs. The different model-predicted micro-porosity data are also validated through experimental data. Statistical tests and Monte-Carlo reliability analysis are additionally utilized to evaluate the performances of the employed algorithms. IBk and MLP are overall found to perform well.

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“…Fusion welding of Cu to SS by conventional fusion welding is challenging due to the differences in physical, chemical and thermo-mechanical properties and limited solid solubility [4]. Electron beam welding (EBW) is a fusion welding process that yields good mechanical and metallurgical properties in comparison to other welding techniques [5,6] and offers specific advantages like high power density, high depth to width ratio and fewer defects formation [7]. During welding, weld metal undergoes through the complicated thermal cycle and liquid flow which significantly affects the microstructure and mechanical properties within the fusion zone (FZ) that could be influenced by grains orientation and texture formation [8].…”

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confidence: 99%