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Study of micro-porosity in electron beam butt welding

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Abstract

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 using 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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The data and materials will be made available to others on request, after it is accepted for publications.

Abbreviations

\({A}_{ar}\) :

Pore surface area

\({D}_{p}\) :

Average pore diameter

E :

Heat input per unit length

I :

Beam current

L :

Total number of welding conditions

\({N}_{p}\) :

Number of pores

\({N}_{success}\) :

Number of successful conditions

\({N}_{total}\) :

Total number of conditions

\({O}_{{ok}^{^{\prime}}l}\) :

Predicted outputs

\({\overline{O} }_{o{k}^{^{\prime}}l}\) :

Mean of outputs

\({P}_{occurrence}\) :

Probability of success

\({R}^{2}\) :

Correlation coefficient

\({S}_{phry}\) :

Average pore sphericity

\({T}_{{ok}^{^{\prime}}l}\) :

Target outputs

U :

Welding speed

V :

Accelerating voltage

\({V}_{ol}\) :

Pore volume

AAPD :

Average absolute percent deviation

ANN :

Artificial neural network

C-LBW :

Continuous laser beam welding

CV :

Cross-validation

DOE :

Design of experiments

EBW :

Electron beam welding

GTAW :

Gas tungsten arc welding

IBk :

Instance-based k-nearest neighbor algorithm

k-NN :

k-Nearest neighbor

LBW :

Laser beam welding

LWL :

Locally weighted learning

M5P :

Model trees regression following M5 algorithm

MLAs :

Machine learning algorithms

MLP :

Multi-layer perceptron

NDT :

Non-destructive testing

OM :

Optical microscopy

P-LBW :

Pulsed laser beam welding

REPTree :

Reduced error pruning tree

RF :

Random forest

RMSE :

Root mean square error

SEM :

Scanning electron microscopy

SVR :

Support vector regression

XCT :

X-ray computed tomography

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Funding

The first three authors received the funding (in the form of fellowship) from the Ministry of Human Resource Development (MHRD) (now, Ministry of Education), Government of India for carrying out the study.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Debasish Das, Amit Kumar Das & Dilip Kumar Pratihar

  2. Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Soumitra Kumar Dinda & Gour Gopal Roy

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  1. Debasish Das
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  2. Soumitra Kumar Dinda
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  3. Amit Kumar Das
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Contributions

Debasish Das: Experiment, Data Collection, Machine learning-based Modeling, Paper Writing. Soumitra Dinda: Experiment, Data collection; Paper Writing. Amit Kumar Das: Modeling, Paper Writing. Dilip Kumar Pratihar: Supervision, Reviewing and Editing. Gour Gopal Roy: Supervision, Reviewing and Editing.

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Correspondence to Dilip Kumar Pratihar or Gour Gopal Roy.

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Das, D., Dinda, S.K., Das, A.K. et al. Study of micro-porosity in electron beam butt welding. Int J Adv Manuf Technol 121, 4583–4600 (2022). https://doi.org/10.1007/s00170-022-09359-x

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