Geometric Model of the Weld Bead in DC and Square AC Submerged Arc Welding of 2.25 Cr-1 Mo Heat Resistant Steel

Mohanty, Uttam Kumar; Sharma, Abhay; Abe, Yohei; Fujimoto, Takahiro; Nakatani, Mitsuyoshi; Kitagawa, Akikazu; Tanaka, Manabu; Suga, Tetsuo

doi:10.1007/978-981-32-9433-2_38

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…In this context, it is crucial to look at the effects of welding operation ultimate mechanical properties of the joint. Thus optimization of process parameters plays a crucial role in achieving weldment of desired strength, weld bead characters [16], and defect-free joint [17]. The key factors for achieving the desired integrity of the weldment, including hardness and strength, are the geometric features of the weld bead, like penetration depth (P), bead width (BW), bead height/reinforcement (R), and depth-to-width ratio (D/w) [18].…”

Section: Introductionmentioning

confidence: 99%

Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance

Devaraj

Ziout

Qudeiri

2021

Metals

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The quality of a welded joint is determined by key attributes such as dilution and the weld bead geometry. Achieving optimal values associated with the above-mentioned attributes of welding is a challenging task. Selecting an appropriate method to derive the parameter optimality is the key focus of this paper. This study analyzes several versatile parametric optimization and prediction models as well as uses statistical and machine learning models for further processing. Statistical methods like grey-based Taguchi optimization is used to optimize the input parameters such as welding current, wire feed rate, welding speed, and contact tip to work distance (CTWD). Advanced features of artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) models are used to predict the values of dilution and the bead geometry obtained during the welding process. The results corresponding to the initial design of the welding process are used as training and testing data for ANN and ANFIS models. The proposed methodology is validated with various experimental results outside as well as inside the initial design. From the observations, the prediction results produced by machine learning models delivered significantly high relevance with the experimental data over the regression analysis.

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Section: Introductionmentioning

confidence: 99%

Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance

Devaraj

Ziout

Qudeiri

2021

Metals

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Parametric study on AC square waveform welding

Mohanty

Sharma

Abe

et al. 2022

Materials Today: Proceedings

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Determining the arc temperature in submerged arc welding using the Bartels method

Goett¹,

Gericke²,

Henkel³

et al. 2020

J. Phys. D: Appl. Phys.

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Never before has an optical method been used to determine the arc temperature inside a submerged arc welding cavern with frequencies up to 5 kHz. To be able to do that, a combination of high-speed imaging and spatially resolved high-speed spectroscopy with up to 5000 fps has been performed. A DCEP (direct current electrode positive) process with 600, 800, 900 and 1000 A, and an AC (alternating current) process at 800 A were included in this research. The Bartels method has been used to calculate these temperatures for the first time. It generated temperatures from approximately 7000–9000 K. Also, it was found that a decrease of arc temperature for rising currents appeared until 900 A. After that, it is reversed for currents higher than 900 A.

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Geometric Model of the Weld Bead in DC and Square AC Submerged Arc Welding of 2.25 Cr-1 Mo Heat Resistant Steel

Cited by 3 publications

References 14 publications

Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance

Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance

Parametric study on AC square waveform welding

Determining the arc temperature in submerged arc welding using the Bartels method

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