Machine learning method to predict and analyse transient temperature in submerged arc welding

Sarkar, Shib Sankar; Das, Ankit; Paul, Siddhartha; Mali, Kalyani; Ghosh, Aniruddha; Sarkar, Ram; Kumar, Arvind

doi:10.1016/j.measurement.2020.108713

Cited by 19 publications

(3 citation statements)

References 36 publications

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“…There are three main characteristics of k-means that make it very efficient for solving engineering problems; however, these same characteristics are also frequently seen as its most significant drawbacks. These characteristics include [28]:…”

Section: K-mean Clustering and The Local Outlier Factor (Lof) Modelmentioning

confidence: 99%

Data-Driven Intelligent Model for the Classification, Identification, and Determination of Data Clusters and Defect Location in a Welded Joint

2022

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In this paper, a data-driven approach that is based on the k-mean clustering and local outlier factor (LOF) algorithm has been proposed and deployed for the management of non-destructive evaluation (NDE) in a welded joint. The k-mean clustering and LOF model algorithm, which was implemented for the classification, identification, and determination of data clusters and defect location in the welded joint datasets, were trained and validated such that three (3) different clusters and noise points were obtained. The noise points, which are regarded as the welded joint defects/flaws, allow for the determination of the cluster size, heterogeneity, and silhouette score of the welded joint data. Similarly, the LOF model algorithm was implemented for the detection, visualization, and management of flaws due to internal cracks, porosity, fusion, and penetration in the welded joint. It is believed that the management of welded joint flaws would aid the actualization of the Industry 4.0 concept in the development of lightweight products for manufacturing.

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Section: K-mean Clustering and The Local Outlier Factor (Lof) Modelmentioning

confidence: 99%

Data-Driven Intelligent Model for the Classification, Identification, and Determination of Data Clusters and Defect Location in a Welded Joint

2022

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“…Dejans et al [8] explored the correlation between some of the acoustic waves and the physical events that occurred in the welding through the sound waves collected in the welding. Sarkar et al [9] proposed a machine learning-based -multiple linear regression (MLR) to predict and transient temperature in SAW. Günther et al [10] developed an architecture with deep neural networks and reinforced learning to represent, predict, and control an intelligent laser welding process.…”

Section: Introductionmentioning

confidence: 99%

Real-time prediction model of welding temperature based on LSTM

Cui,

Wang,

Liu

et al. 2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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Temperature variations during the welding process directly affect the melting and solidification process of welding, which has a significant impact on the quality and mechanical properties of the weld. Real-time prediction of the welding temperature field plays a crucial role in solving the problems posed by the instability and complexity of the welding process, and has developed into an important direction in welding technology. In this paper, the real-time temperature variations during the three-layer, three-pass MIG (melt inert-gas) welding of aluminum alloys were monitored in real time. On this basis, a welding temperature prediction model based on the long-short-term memory neural network (LSTM) was proposed to predict the temperature changes in the unwelded region by taking the temperature data of the torch-passed region during the welding process , by analyzing the prediction results in comparison with the actual measured temperature changes, the results show that the method can predict the welding temperature changes better. This method of over-prediction is important for welding quality control, defect prevention and process optimization.

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“…With the increasing demand for ultra-thick steel plates, research on welding technology for ultra-thick materials is being actively conducted. Conventionally, FCAW and submerged arc welding (SAW) have been widely employed for fabricating vessels because they offer advantages such as automation and wide applicability [11][12][13][14] . However, these welding methods entail degradation of productivity when applied to thick plate welding because of the large number of welding deposits 15) .…”

Section: Introductionmentioning

confidence: 99%

A Study on the Microstructure and Mechanical Properties of the Coarse Grain Heat Affected Zone in EH40 Grade High Strength Structural Steels

Jeong¹,

Hong²,

Seo³

et al. 2022

J Weld Join

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In this paper, the effect of the electro gas arc welding (EGW) process with a heat input of 600 kJ/cm on the microstructural and mechanical characteristics was investigated for two types of EH40 high strength hull structural steels, containing Ti and Nb. The coarse grain heat affected zone (CGHAZ), with a peak temperature of 1450 °C, was simulated for both alloys using a material thermal cycle simulator. Tensile and Charpy impact toughness tests were performed to determine the mechanical properties of the CGHAZ, and microstructural analysis was performed using scanning electron microscopy and transmission electron microscopy. Both alloys exhibited a significant loss of impact toughness because of the welding thermal cycle, causing the formation of a brittle phase and the growth of precipitates. The experimental results indicated that although the addition of Ti and Nb had several benefits on mechanical properties, this might be unsuitable for the EGW process owing to the resulting deterioration in impact toughness.

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Machine learning method to predict and analyse transient temperature in submerged arc welding

Cited by 19 publications

References 36 publications

Data-Driven Intelligent Model for the Classification, Identification, and Determination of Data Clusters and Defect Location in a Welded Joint

Data-Driven Intelligent Model for the Classification, Identification, and Determination of Data Clusters and Defect Location in a Welded Joint

Real-time prediction model of welding temperature based on LSTM

A Study on the Microstructure and Mechanical Properties of the Coarse Grain Heat Affected Zone in EH40 Grade High Strength Structural Steels

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