Thermal modelling of alternating current square waveform arc welding

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

doi:10.1016/j.csite.2021.100885

Cited by 13 publications

(6 citation statements)

References 26 publications

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“…Figure 5(a) shows an increasing behavior of T e with the increase in weld current from 50 to 60 A (please refer to conditions A1, A2, and A3 of table 1). This behavior is reasonable, as during the arc welding process, the increase in current leads to an increase in total heat input at a constant frequency [34,35], which reflects an increase in T . 1).…”

Section: Electron Density and Electron Temperature Under Various Join...mentioning

confidence: 79%

Non-invasive optical characterization and estimation of Zn porosity in gas tungsten arc welding of Fe–Al joints using CR model and OES measurements

SRIKAR

Kumar

Kiran

et al. 2023

Plasma Sci. Technol.

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In this study, we employed a non-invasive approach based on the collisional radiative (CR) model and optical emission spectroscopy (OES) measurements for the characterization of gas tungsten arc welding (GTAW) discharge and quantification of Zn-induced porosity during the GTAW process of Fe-Al joints. The OES measurements were recorded as a function of weld current, welding speed, and input waveform. The OES measurements revealed significant line emission from Zn-I in 460 nm to 640 nm and Ar-I in 680 nm to 800 nm wavelength range in all experimental settings. The OES cum CR model approach for Zn-I line emission enabled the simultaneous determination of both essential discharge parameters i.e. electron temperature and electron density. Further, these predictions were used to estimate the Zn-induced porosity using OES-Actinometry on Zn-I emission lines using Ar as actinometer gas. The OES-actinometry results were in good agreement with porosity data derived from an independent approach, i.e., X-ray radiography images. The current study shows that OES-based techniques can provide an efficient route for real-time monitoring of weld quality and estimate porosity during the GTAW process of dissimilar metal joints.

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Section: Electron Density and Electron Temperature Under Various Join...mentioning

confidence: 79%

Non-invasive optical characterization and estimation of Zn porosity in gas tungsten arc welding of Fe–Al joints using CR model and OES measurements

SRIKAR

Kumar

Kiran

et al. 2023

Plasma Sci. Technol.

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“…Similarly, process specific changes need to adopt for accurate prediction of the heat source parameter to reduce error. Mohanty et al implemented it for the alternating current square waveform arc welding with necessary changes for better parameter selection [ 46 ]. This method results in a percentage prediction error not exceeding 10%.…”

Section: Resultsmentioning

confidence: 99%

Heat Source Modeling and Residual Stress Analysis for Metal Directed Energy Deposition Additive Manufacturing

et al. 2022

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The advancement in additive manufacturing encourages the development of simplified tools for deep and swift research of the technology. Several approaches were developed to reduce the complexity of multi-track modeling for additive manufacturing. In the present work, a simple heat source model called concentrated heat source was evaluated for single- and multi-track deposition for directed energy deposition. The concentrated heat source model was compared with the widely accepted Goldak heat source model. The concentrated heat source does not require melt pool dimension measurement for thermal model simulation. Thus, it reduces the considerable time for preprocessing. The shape of the melt pool and temperature contour around the heat source was analyzed for single-track deposition. A good agreement was noticed for the concentrated heat source model melt pool, with an experimentally determined melt pool, using an optical microscope. Two heat source models were applied to multi-track 3D solid structure thermo-mechanical simulation. The results of the two models, for thermal history and residual stress, were compared with experimentally determined data. A good agreement was found for both models. The concentrated heat source model reported less than the half the computational time required for the Goldak model. The validated model, for 3D solid structure thermo-mechanical simulation, was used to analyze thermal stress evolution during the deposition process. The material deposition on the base plate at room temperature results in lower peak temperatures in the layers near the base plate. Consequently, the higher thermal stress in the layers near the base plate was found, compared to the upper layers during the deposition process.

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“…The thermocouples are used to measure the temperature variation in the workpiece's body to obtain the thermal cycle and identify the heat source model parameters based on the welding conditions [12]. The heat source model parameters for different welding processes, e.g., single-wire welding, twin-wire welding [13], square waveform welding [14], need to be adapted for better representation of the process. The heat source models can accurately explain the correlation between welding parameters and weld properties like bead shape and cooling time.…”

Section: Thermal Measurementmentioning

confidence: 99%

Digitization of Welding Processes

Kapil¹,

Quadir²,

Sharma³

2021

Preprint

Self Cite

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Welding processes offer a unique capability with a wide range of applications in industries. In recent times, welding has established itself as a tool for large scale additive manufacturing. In general, the quality and repeatability assurance for welding and specifically for additive manufacturing necessitates integrating process monitoring techniques with existing welding and additive manufacturing processes. The process-specific signals such as welding current fluctuations, temperature, and acoustic, generated during the welding operations, make them a suitable candidate for digitization. This chapter comprehensively describes the process monitoring techniques relevant to welding and additive manufacturing. Firstly, various sensors used during welding are described for their construction and working. Subsequently, specific applications of the sensors in digitizing the welding processes are presented.

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Thermal modelling of alternating current square waveform arc welding

Cited by 13 publications

References 26 publications

Non-invasive optical characterization and estimation of Zn porosity in gas tungsten arc welding of Fe–Al joints using CR model and OES measurements

Non-invasive optical characterization and estimation of Zn porosity in gas tungsten arc welding of Fe–Al joints using CR model and OES measurements

Heat Source Modeling and Residual Stress Analysis for Metal Directed Energy Deposition Additive Manufacturing

Digitization of Welding Processes

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