Cooling rate predictions and its correlation with grain characteristics during electron beam welding of stainless steel

Proceedings of the Institution of Mechanical Engineers, Part C:

Pratihar

et al. 2020

Self Cite

In the present study, machine learning algorithms have been used to predict residual stress during electron beam welding of stainless steel using the information of input process parameters and natural frequency of vibrations. Accelerating voltage, beam current and welding speed have been considered as input process parameters. Both residual stress and natural frequencies of vibration of the weld obtained using each set of the input parameters are measured experimentally. A number of machine learning algorithms, namely M5 algorithm-based Model Trees Regression, Random forest, Support Vector Regression, Reduced Error Pruning Tree, Multi-layer perceptron, Instance-based k-nearest neighbor algorithm, and Locally weighted learning have been used for the said purpose. Support vector regression and Locally weighted learning are found to perform consistently good and bad, respectively. The predicted welding residual stresses have been validated experimentally through X-ray diffraction (XRD) and good agreements are obtained. In addition, statistical tests are conducted, and the estimated reliability values of the employed models are analyzed through Monte-Carlo simulations.

Section: Methodsmentioning

confidence: 99%

Prediction of residual stress in electron beam welding of stainless steel from process parameters and natural frequency of vibrations using machine-learning algorithms

Proceedings of the Institution of Mechanical Engineers, Part C:

Pratihar

et al. 2020

Self Cite

“…The welded joint, thus, often acts as a potential site of failure [2]. In particular, Electron Beam Welding (EBW) process is often subjected to a higher cooling rate, and the strong influence of Marangoni, Lorentz, and buoyant forces [3][4][5]. Thus, under such complex circumstances, weld defect estimation becomes important for quality assurance.…”

Section: Introductionmentioning

confidence: 99%

Study of micro-porosity in electron beam butt welding

Dinda

Int J Adv Manuf Technol

et al. 2022

Self Cite

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.

Journal of Petroleum and Mining Engineering

“…Where, Na is the nugget area in mm2, A is welding current in Amp, and S is the welding speed in mm/sec. The cooling rate is the temperature loss per unit time, and in the range 800 to 500oC it is important for the phase transformation of stainless steel, and it plays an important role in determining the final microstructure of weld metal and its properties [17]. The cooling rate and cooling time can be calculated with Eq.3 and Eq.4 [18,19], respectively.…”

Section: Introductionmentioning

confidence: 99%

Effect of Heat Input and Shielding Gas on the Performance of 316 Stainless Steel Gas Tungsten Arc Welding

Fouad

El-Nikhaily

Ahmed

et al. 2020

The effect of nitrogen addition and heat input on weld metal microstructure and mechanical properties of 316 stainless steel is studied. Autogenous gas tungsten arc welding (GTAW) is employed by adding up to 2 vol.% N2 in Ar. Welding speed and heat input rate are measured as functions of gas composition. Weld defects are examined by radiographic testing, and weld metal microstructure is studied by optical microscopy. Mechanical properties of welds are determined by uniaxial tensile testing, hardness measurements, and bending test. Weld dendritic structure is refined by increasing N2 content in Ar. The mechanical properties and cooling rate are lower with increasing heat input. Besides, adding nitrogen to argon shielding gas leads to higher values of the ultimate tensile strength and hardness. The tensile strength, yield stress and elongation percent of welds depends strongly on the heat input and nitrogen content in shielding gas. This is discussed on the basis of microstructural characterization. Moreover, the weld nugget area, cooling time and solidification time increase with increasing heat input and nitrogen content. Finally, after applying the bending test up to 180o no cracks, tearing or surface defects could be observed on welded samples.