Multi-objective optimizations of the Q355C steel gas metal arc welding process based on the grey correlation analysis

Weng, Huajing; Jiang, Jibin; Feng, Meiyan; Yao, Mingpu; Chen, Changrong; Lian, Guofu

doi:10.1007/s00170-022-09547-9

Cited by 14 publications

(6 citation statements)

References 26 publications

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“…Overall, Figure 3 shows an increase in the track width ( w ) and height ( h ) with an increase in the WFR and decrease in the PTS. The documented literature [17,18] suggests that a typical range of aspect ratio (w/h) of around 1.5–2.5 can yield a fair surface finish during gas metal arc and plasma arc-based deposition processes. The aspect ratio ( w / h ) of the deposited tracks, which are shown in Figure 3(a–f), is in the range of 1.6–2.4 and thus, follow the trend as suggested in the literature [17,18].…”

Section: Resultsmentioning

confidence: 99%

“…The documented literature [17,18] suggests that a typical range of aspect ratio (w/h) of around 1.5–2.5 can yield a fair surface finish during gas metal arc and plasma arc-based deposition processes. The aspect ratio ( w / h ) of the deposited tracks, which are shown in Figure 3(a–f), is in the range of 1.6–2.4 and thus, follow the trend as suggested in the literature [17,18]. In subsequent sections, the melt pool asymmetry during the deposition of multiple tracks is presented for a constant WFR of 6 m/min and two different PTS of 4 mm/s and 6 mm/s with four different hatch spacing.…”

Section: Resultsmentioning

confidence: 99%

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Monitoring melt pool asymmetry in gas metal arc-directed energy deposition

Chaurasia

Goecke

2023

Science and Technology of Welding and Joining

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Cross-sectional asymmetry of melt pools along adjacent tracks results in an uneven build surface in gas metal arc-directed energy deposition. We present here a novel attempt on real-time monitoring of melt pool cross-sectional asymmetry in terms of its lateral and angular deviations from the track centreline using a two-colour quotient thermal camera. The melt pool lateral and angular deviations are found to be 2-3 mm, and 8-12°, respectively, for the range of process conditions. A unique analytical methodology is proposed that shows a hatch spacing of around 72-76% of the width of a deposited track can reduce melt pool asymmetry and layer height variations along adjacent tracks, which is also observed experimentally.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Monitoring melt pool asymmetry in gas metal arc-directed energy deposition

Chaurasia

Goecke

2023

Science and Technology of Welding and Joining

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“…Their application is widespread and commonly used to increase penetration, reduce energy input, enhance mechanical properties, and in uence the microstructure of the weld bead [10] . GMAW process [11] was developed in the last century as a highe ciency welding technology due to advantages such as high production e ciency, low welding cost, wide application range, good welding quality, simple operation, and easy mastering.…”

Section: Introductionmentioning

confidence: 99%

Effect of activating fluxes on geometry, hardness, and microstructure of 316L stainless steel in GMAW

WANG,

Klaric

2024

Preprint

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Activating fluxes (AF) can be applied in the welding process to improve the morphology, microstructure, and mechanical properties, such as hardness, tensile strength, yield strength, etc. In regard to published research on AF application in Tungsten Inert Gas (TIG) welding, there are limited studies concerning the Gas Metal Arc Welding (GMAW) process. This gap in research has prompted investigations aimed at finding out the AF’s influence during the GMAW process. The purpose of this paper is to apply three AF (SiO2, TiO2, and CaCO3) in 316L stainless steel GMAW processing and to analyze their influence on weld bead geometry, hardness and microstructure. The results showed that the highest penetration and the smallest width can be obtained using TiO2 as AF, and the highest reinforcement can be obtained by CaCO3 as AF. They also indicated that AF addition could significantly increase after-welding hardness, which might be caused by the microstructure changes. The microstructure observation revealed that the welding area without AF was mainly composed of austenite, and due to the addition of AF increased the welding temperature, which caused the martensite structure to be found in these samples. The heat treatment was introduced to reduce the hardness since the too big and uneven hardness would bring negative consequences such as brittleness. The after-HT analysis showed that HT can reduce the hardness effectively and can improve the uniformity of whole weld bead. Additionally, it was found that samples with AF were more sensitive to HT. This study concludes that AF can be applied in GMAW welding process and can influence the weld bead significantly.

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“…As early as the mid-1960s, a large number of researchers began to study the expert system. Welding technology is the product of multi-disciplines, ranging from weldability analysis before welding [2,3] and preparation of welding process documents [4] to the selection of process parameters in the welding process [5][6][7][8] and real-time control of welding [9] , defect diagnosis and treatment after welding [10] , welding quality assessment [11,12] and calculation of welding material consumption [13] . Each stage involves a large amount of data, knowledge and models, which is an ideal field for expert system application [14,15] .…”

Section: Introductionmentioning

confidence: 99%

GMAW welding procedure expert system based on machine learning

Wang¹,

Chen²,

Sun³

et al. 2023

Intell Robot

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In order to simplify the robot preparation before welding and improve the automation of the whole welding process, an intelligent expert system for Gas Metal Arc Welding is designed in this paper. In the system, the user inputs the initial welding information and the output interface displays suitable welding procedure parameter schemes. The user can choose the schemes according to the actual requirements or directly generate the welding procedure specification required by the enterprise format for direct use. In addition, the system also combines the database technology and XGBoost algorithm in the field of machine learning, migrates the model trained on the data set to predict the welding raw data, accumulates more data for daily use to optimize the model, which makes the whole system more systematic and intelligent, and achieves the goal of more accurate use.

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Multi-objective optimizations of the Q355C steel gas metal arc welding process based on the grey correlation analysis

Cited by 14 publications

References 26 publications

Monitoring melt pool asymmetry in gas metal arc-directed energy deposition

Monitoring melt pool asymmetry in gas metal arc-directed energy deposition

Effect of activating fluxes on geometry, hardness, and microstructure of 316L stainless steel in GMAW

GMAW welding procedure expert system based on machine learning

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