Monte Carlo simulation and experimental measurements of grain growth in the heat affected zone of 304 stainless steel during multipass welding

Chen, Xizhang; Chen, Xing; Xu, Hui; Madigan, Bruce

doi:10.1007/s00170-015-7024-3

Cited by 25 publications

(8 citation statements)

References 9 publications

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“…A weld with integrity must reach the required penetration for joining and simultaneously present the minimum HAZ possible. Rafieazad et al [25] and Chen et al [26] mentioned that this region is critical due to brittle phases and grain growth.…”

Section: Optimizationmentioning

confidence: 99%

“…However, it also presented a higher HAZ volume, which is not positive. Choosing this parameter for a joint could lead to a lack of integrity due to problems such as grain growth [24,25]. Therefore, the best application would be for a partial penetration joint (A to E).…”

Section: Optimizationmentioning

confidence: 99%

“…One should know that is not enough to achieve just a needed penetration. The HAZ must be minimized since it may present brittle phases [24] and grain growth regions [25]. Grajcar et al [26] reported a direct relation between welding energy and HAZ width.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity analysis and multi-objective optimization of tungsten inert gas (TIG) welding based on numerical simulation

Paes

Andrade

Lobato

et al. 2022

Int J Adv Manuf Technol

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In welding processes, many factors contribute in achieving a required quality of the welds. Those factors are numerous and they may interact with each other, affecting response parameters such as welding penetration and the Heat Affected Zone (HAZ) size. Some factors are more important while the influence of others is negligible.To find an optimum factor combination in order to maximize penetration and minimize the HAZ is not an easy task. This contribution is aimed to evaluate the influence of welding energy (E) versus the influence of current (I) and welding speed (Vw) on the penetration and HAZ volume in the autogenous Tungsten Inert Gas (TIG) welding process. For this purpose, phenomenological and empirical models are proposed. The first considers an in-house Finite Volume numerical model, and the second is based on Response Surface Method. A sensitivity analysis of the empirical model using two strategies is also performed. In addition, to determine the best-operating conditions, a multi-objective optimization problem is proposed and solved. The presented empirical models were found to provide good concordance in terms of coefficient of determination and p-value, indicating its significance. Each model (with one or more independent variables) represents detailed information about the physical process and can be used for optimization. The sensitivity analysis demonstrates that the current affects penetration and HAZ volume much stronger than the welding speed does. Physically, this is due to the fact current has linear (arc coupling) and non-linear (Joule effect and pressure gradient) influence, and the welding speed contributes linearly, modulating the heat conduction. Finally, it was demonstrated a compromise between the penetration and the HAZ volume by addressing multi-objective optimization. In this context, point C (I = 250 A; VW= 24.8 cm/min) of the Pareto curve is the optimal option for operation since it provides a lower relative HAZ volume while keeping the same penetration and higher productivity (welding speed).

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Section: Optimizationmentioning

confidence: 99%

Section: Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Sensitivity analysis and multi-objective optimization of tungsten inert gas (TIG) welding based on numerical simulation

Paes

Andrade

Lobato

et al. 2022

Int J Adv Manuf Technol

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“…The time period of the wind measurement is always restricted which bring about the problem of the selection of the wind sampling time. In this paper, Monte Carlo (MC) simulation is performed to acquire the random discrete data obeying the continuous Weibull distribution model (Chen et al, 2015;Hu et al, 2020). Sample numbers with three magnitudes, i.e., 1000, 10,000 and 100,000, are adopted for the MC simulation to mimic the wind data measured under various time periods.…”

Section: Wind Data Prediction Modelmentioning

confidence: 99%

A new three-dimensional wake model for the real wind farm layout optimization

Luo

Xie

et al. 2021

Energy Exploration & Exploitation

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The utilization of wind energy has attracted extensive attentions in the last few decades around the world, providing a sustainable and clean source to generate electricity. It is a common phenomenon of wake interference among wind turbines and hence the optimization of wind farm layout is of great importance to improve the wind turbine yields. More specifically, the accuracy of the three-dimensional wake model is critical to the optiamal design of a real wind farm layout considering the combinatorial effect of wind turbine interaction and topography. In this paper, a novel learning-based three-dimensional wake model is proposed and subsequently validated by comparison to the high-fidelity wake simulation results. Moreover, due to the fact that the inevitable deviation of actual wind scenario from the anticipated one can greatly jeopardize the wind farm optimization outcome, the inaccuracy of wind condition prediction using the existing meteorologic data with limited-time measurement is incorporated into the optimization study. Different scenarios including short-, medium-, and long-term wind data are studied specifically with the wind speed/direction prediction errors of [Formula: see text] 0.25 m/s, [Formula: see text] 5.62 [Formula: see text], [Formula: see text] 0.08 m/s, [Formula: see text] 1.75 [Formula: see text] and [Formula: see text] 0.025 m/s, [Formula: see text] 0.56 [Formula: see text], respectively. An advanced objective function which simultaneously maximizes the power output and minimizes the power variance is employed for the optimization study. Through comparison, it is found that the optimized wind farm layout yields over 210 kW more total power output on average than the existed wind farm layout, which verifies the effectiveness of the wind farm layout optimization tool. The results show that as the measurement time for predicting the wind condition gets longer, the total wind farm power output average increases while the error of power output prediction decreases. For the wind farm with 20 wind turbines installed, the individual power output is above 500 kW with an error of 90 kW under the short-term wind [Formula: see text] 0.25 m/s, [Formula: see text] 5.62 [Formula: see text], while it is above 530 kW with an error of 10 kW under the long-term wind [Formula: see text] 0.025 m/s, [Formula: see text] 0.56 [Formula: see text].

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“…Besides, Manidipto et al [14] investigated the grain structure with diverse modes of metal transfer on austenitic stainless steel. Chen et al [15] researched the grain growth in HAZ of 304 stainless steel during the multi-pass via the Monte Carlo method. However, these studies had the following disadvantages: (1) the measuring of the temperature field is difficult with contact temperature measurements, especially the temperature of the fusion area; and (2) the mechanisms of the grain growth in ultra-pure ferritic stainless steel are not clear.…”

Section: Introductionmentioning

confidence: 99%

Finite-Element Thermal Analysis and Grain Growth Behavior of HAZ on Argon Tungsten-Arc Welding of 443 Stainless Steel

Wang

Deng

Zheng

et al. 2016

Metals

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This paper presents a numerical and infrared experimental study of thermal and grain growth behavior during argon tungsten arc welding of 443 stainless steel. A 3D finite element model was proposed to simulate the welding process. The simulations were carried out via the Ansys Parametric Design Language (APDL) available in the finite-element code, ANSYS. To validate the simulation accuracy, a series of experiments using a fully-automated welding process was conducted. The results of the numerical analysis show that the simulation weld bead size and the experiment results have good agreement. The grain growth in the heat-affected zone of 443 stainless steel is influenced via three factors: (1) the thermal cycle experienced; (2) grain boundary migration; and (3) particle precipitation. Grain boundary migration is the main factor. The modified coefficient k of the grain growth index is calculated. The value is 1.16. Moreover, the microhardness of the weld bead softened slightly compared to the base metal.

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Monte Carlo simulation and experimental measurements of grain growth in the heat affected zone of 304 stainless steel during multipass welding

Cited by 25 publications

References 9 publications

Sensitivity analysis and multi-objective optimization of tungsten inert gas (TIG) welding based on numerical simulation

Sensitivity analysis and multi-objective optimization of tungsten inert gas (TIG) welding based on numerical simulation

A new three-dimensional wake model for the real wind farm layout optimization

Finite-Element Thermal Analysis and Grain Growth Behavior of HAZ on Argon Tungsten-Arc Welding of 443 Stainless Steel

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