Thermal Efficiency Analysis for Laser-Assisted Plasma Arc Welding of AISI 304 Stainless Steel

Hipp, Dominik; Mahrle, Achim; Beyer, Eckhard; Jäckel, S.; Hertel, M.; Füssel, Uwe

doi:10.3390/ma12091460

Cited by 18 publications

(4 citation statements)

References 28 publications

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“…The prevailing expediency of such a method is butt welding of blanks with edges of 1-10 mm in thickness [15]. It is exactly with such thicknesses that the following characteristic advantages of the laser-plasma welding process can be ensured: considerable process speeds, reduced residual strains and stresses, mechanical properties of the produced joints becoming close to those of the base metal, and lowered equipment cost due to the partial replacement of the laser power by plasma power [16].…”

Section: Introductionmentioning

confidence: 99%

Formation of Stainless Steel Welded Joints Produced with the Application of Laser and Plasma Energy Sources

Shevchenko,

Korzhyk,

Gao

et al. 2024

Metals

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The objective of this study is to investigate the formation of the structure and stress–strain state in the joints of AISI 304 stainless steel with a thickness of 2 mm and produced by welding with laser and plasma energy sources. It is established that the microhardness and parameters of the grain and subgrain structures of the welded joint material differ with respect to the dimensions of crystallites, grains, and subgrains according to the welding process. It is shown that, in terms of structure formation, including substructural features, the most favorable structures of 2 mm AISI 304 welded joints are formed by laser–plasma welding. It is predicted that the residual stressed state is less localized with the application of laser–plasma welding than laser welding, and it is characterized by a lower level of residual stresses compared to plasma welding. In all the cases, the maximal stress values are concentrated in the HAZ, and the value obtained using laser–plasma welding is in an intermediate position (431.7 MPa) between those of the laser (443 MPa) and plasma (413.7 MPa) processes. With laser–plasma and laser welding, displacements (deformations) are minimal and close to 0.2 mm. The method of electron speckle interferometry was used, and the results reveal that the error between the calculated and experimental values of equivalent stresses is no more than 6%, which is acceptable. The results of mechanical testing show that, under uniaxial tension, the strength of the welded joints made of AISI 304 steel using laser–plasma and laser welding is the highest and equal to 97% of that of the base metal.

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Section: Introductionmentioning

confidence: 99%

Formation of Stainless Steel Welded Joints Produced with the Application of Laser and Plasma Energy Sources

Shevchenko,

Korzhyk,

Gao

et al. 2024

Metals

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“…In the existing researches, four kinds of sensors are mainly used to monitor the laser welding process, such as visual [14][15][16], acoustic [17][18][19], optical [20,21] and thermal [22,23] sensors. These sensors are used to capture the changes of characteristic quantities during the welding process [24][25][26][27] and establish a correlation between the changing trends of characteristic quantities and the final welding effect.…”

Section: Introductionmentioning

confidence: 99%

Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion

Fan

Peng

Zhou

et al. 2021

Sensors

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Most of the existing laser welding process monitoring technologies focus on the detection of post-engineering defects, but in the mass production of electronic equipment, such as laser welding metal plates, the real-time identification of defect detection has more important practical significance. The data set of laser welding process is often difficult to build and there is not enough experimental data, which hinder the applications of the data-driven laser welding defect detection method. In this paper, an intelligent welding defect diagnosis method based on auxiliary classifier generative adversarial networks (ACGAN) has been proposed. Firstly, a ten-class dataset consisting of 6467 samples, was constructed, which originate from the optical and thermal sensory parameters in the welding process. A new structured ACGAN network model is proposed to generate fake data similar to the true defect feature distributions. In addition, in order to make the difference between different defects categories more obvious after data expansion, a data filtering and data purification scheme was proposed based on ensemble learning and an SVM (support vector machine), which is used to filter the bad generated data. In the experiments, the classification accuracy can reach 96.83% and 85.13%, for the CNN (convolutional neural network) algorithm model and ACGAN model, respectively. However, the accuracy can further improve to 97.86% and 98.37% for the fusion models of ACGAN-CNN and ACGAN-SVM-CNN models, respectively. The results show that ACGAN can not only be used as an algorithm model for classification, but also be used to achieve superior real-time classification and recognition through data enhancement and multi-model fusion.

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“…Zhou et al [11], Chen et al [12], and Casalino et al [13] studied the laser welding-brazing process of Titanium-Aluminum dissimilar metals. Silva Leite et al [14] and Hipp et al [15] discussed the laser welding process and mechanism of various stainless steels.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Correlation between Thermal Cycling and the Microstructures of X100 Pipeline Steel Laser-Welded Joints

Wang

Wang²,

Yin

et al. 2019

Materials

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Due to the limitations of the energy density and penetration ability of arc welding technology for long-distance pipelines, the deterioration of the microstructures in the coarse-grained heat-affected zone (HAZ) in welded joints in large-diameter, thick-walled pipeline steel leads to insufficient strength and toughness in these joints, which strongly affect the service reliability and durability of oil and gas pipelines. Therefore, high-energy-beam welding is introduced for pipeline steel welding to reduce pipeline construction costs and improve the efficiency and safety of oil and gas transportation. In the present work, two pieces of X100 pipeline steel plates with thicknesses of 12.8 mm were welded by a high-power robot laser-welding platform. The quantitative correlation between thermal cycling and the microstructure of the welded joint was studied using numerical simulation of the welding temperature field, optical microscopy (OM), and scanning electron microscopy (SEM) with energy-dispersive spectroscopy (EDS). The results show that the heat-source model of a Gaussian-distributed rotating body and the austenitization degree parameters are highly accurate in simulating the welding temperature field and characterizing the austenitization degree. The effects of austenitization are more significant than those of the cooling rate on the final microstructures of the laser-welded joint. The microstructure of the X100 pipeline steel in the HAZ is mainly composed of acicular ferrite (AF), granular bainite (GB), and bainitic ferrite (BF). However, small amounts of lath martensite (LM), upper bainite (UB), and the bulk microstructure are found in the columnar zone of the weld. The aim of this paper is to provide scientific guidance and a reference for the simulation of the temperature field during high-energy-beam laser welding and to study and formulate the laser-welding process for X100 pipeline steel.

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Thermal Efficiency Analysis for Laser-Assisted Plasma Arc Welding of AISI 304 Stainless Steel

Cited by 18 publications

References 28 publications

Formation of Stainless Steel Welded Joints Produced with the Application of Laser and Plasma Energy Sources

Formation of Stainless Steel Welded Joints Produced with the Application of Laser and Plasma Energy Sources

Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion

Quantitative Correlation between Thermal Cycling and the Microstructures of X100 Pipeline Steel Laser-Welded Joints

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