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

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.

Section: Study That Is Multi-layer Perceptron (Mlp) and Support Vecto...mentioning

confidence: 99%

“…It is to be noted that the welding samples are cleaned with acetone to remove all the dirt particles prior to carrying out the welding. Experiments are conducted on an 80kV-150mA EBW set-up, developed by Bhabha Atomic Research Centre, Mumbai, India [34,37] at IIT Kharagpur, India (refer to Fig. 1(a)).…”

Section: Experimentationmentioning

confidence: 99%

Study of micro-porosity in electron beam butt welding

Dinda

Int J Adv Manuf Technol

et al. 2022

Self Cite

“…) [34], where 2 , , , L, ′ and ′ denote the correlation coefficient, root mean square error, average absolute percent deviation, total number of welding conditions, target and predicted outputs of l th condition, respectively.…”

Section: Data Augmentationmentioning

confidence: 99%

“…The training of the MLAs is carried out utilizing data corresponding to (K-1) groups, following validation and performance evaluation using the left-out group. This MLA-based training and testing is conducted K times, that is 2, 5 or 10 times[34]. As a result, entire data set is utilized to evaluate the performance of the MLA-based modelling.…”

mentioning

confidence: 99%

Study of Micro-porosity in Electron Beam Butt Welding

Das¹,

Dinda²,

Das³

et al. 2021

Preprint

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 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.

“…Sagai et al successfully used process parameters to predict mechanical properties by using the stochastic and nonlinear parallel machining neural network model [ 25 ]. Das et al used the machine learning algorithm to predict the welding residual stress of stainless steel [ 26 ]. Mathew et al used the fuzzy neural network (FNN) to predict the welding residual stress of pressure vessels and achieved good results [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Welding Deformation and Residual Stress of a Thin Plate by Improved Support Vector Regression

Liu

Ren

et al. 2021

Scanning

Thin plates are widely utilized in aircraft, shipbuilding, and automotive industries to meet the requirements of lightweight components. Especially in modern shipbuilding, the thin plate structures not only meet the economic requirements of shipbuilding but also meet the strength and rigidity requirements of the ship. However, a thin plate is less stable and prone to destabilizing deformation in the welding process, which seriously affects the accuracy and performance of the thin plate welding structure. Therefore, it is beneficial to predict welding deformation and residual stress before welding. In this paper, a thin plate welding deformation and residual stress prediction model based on particle swarm optimization (PSO) and grid search(GS) improved support vector regression (PSO-GS-SVR) is established. The welding speed, welding current, welding voltage, and plate thickness are taken as input parameters of the improved support vector regression model, while longitudinal and transverse deformation and residual stress are taken as corresponding outputs. To improve the prediction accuracy of the support vector regression model, particle swarm optimization and grid search are used to optimize the parameters. The results show that the improved support regression model can accurately evaluate the deformation and residual stress of butt welding and has important engineering guiding significance.