Temperature and Stress Evaluation during Friction Stir Welding of Inconel 718 Alloy Using Finite Element Numerical Simulation

Saha, Ranamay; Biswas, Pankaj

doi:10.1007/s11665-021-06313-y

Cited by 16 publications

(4 citation statements)

References 20 publications

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“…e shear strength is the opposite, decreasing first and then decreasing again [15][16][17][18][19][20].…”

Section: Discussionmentioning

confidence: 97%

Analyzing Welding Performance of Metal Using Artificial Neural Network

2022

Mobile Information Systems

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In recent years, the speed of modernization construction in China has been exponentially growing. The trend of high parameters, large capacity, and large-scale development of the welding structure has been promoted. It needs higher requirements on the type and quality of the welding materials. Most of the welding materials are imported to China. The main reason is that China still follows the traditional design method. The quality of the welding materials is also low. The design of metal welding materials involves many factors and properties. There is no fixed-function relationship between the properties and components of the welding materials. This makes it difficult to design metal welding materials. The emergence of neural network algorithms provides a new way to analyze the weldability of metal materials. In this paper, BP (backpropagation) network is used to analyze the welding performance of metals. The tensile test of welded joints is carried out through training test samples. The results show that the tensile strength and yield strength of metal materials are about 500 MPa (megapascals) and 400 MPa, respectively. For further analysis of the influence of welding current, electrode pressure, and power-on time on the tensile and shear strength of metal materials, a shear test and tension test were used. With the increase of welding current, the shear strength of spot welding continuously increased. When the welding current was 10,000A (Ampere), the shear strength decreased rapidly from 24.25 MPa to 18.84 MPa. After prolonging the welding time, at first, both tensile strength and shear strength increase and then decrease. When the welding pressure increases from 32psi to 48psi, the tensile strength increases from 16.47 MPa to 24.52 MPa and then decreases continuously to 17.26 MPa, whereas the shear strength decreases first and then increases.

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“…e shear strength is the opposite, decreasing first and then decreasing again [15][16][17][18][19][20].…”

Section: Discussionmentioning

confidence: 97%

Analyzing Welding Performance of Metal Using Artificial Neural Network

2022

Mobile Information Systems

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“…In the paper a limited approach was used, that was based only on bounding the simulation results with the shape of real welded joint and HAZ cross-section. As FEM modelling is generally used to predict the stress distribution in welded joints [28,29], it was applied in the research to obtain a reliable and accurate temperature distribution on the surface of the workpiece. Even using this limited approach it was found, that naïve application of theoretic values of thermal efficiency can lead unexpected results, when the temperature distribution remains unreliable.…”

Section: Discussionmentioning

confidence: 99%

FEM-Based Thermogram Correction for Inconel 625 Joint Hardness Clustering

et al. 2022

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Assessing the temperature of the joint in on-line mode is a vital task that is demanded to characterize the formations of terns formations that are taking place in a joint and result in reaching necessary properties of the joint. Arc welding generates a high amount of heat that is reflected by the metallic surface of the welded object. In the paper, a temperature measurement credibility increase method is described and evaluated. The proposed method is used to reduce the influence of the reflected temperature of the hot torch and the arc on the temperature distribution observed on the surface of the welded joint using an infrared camera. The elaborated approach is based on comparison between infrared observation of the solidifying weld and precisely performed finite element method (FEM) simulation. The FEM simulations were calibrated according to the geometry of the fusion zone. It allows to precisely model heat source properties. The best-reflected temperature correction map was selected and applied to obtain a temperature representation that differs from the FEM baseline by less than 10 °C. Precise temperature values allowed us to cluster welded joints in 3D feature space (temperature, hardness, linear energy). It was found that by using the k-means clustering method it is possible to distinguish between correct and faulty (in terms of too low mechanical properties) joints.

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“…This is due to the fact that the friction force between the tool and the base metal is complemented by the reverse of welding. The retreating side temperature is lower due to the direction of friction of the tool and base metal in the direction of welding [23] [24]. The highest temperature occurs when the tool is in the center position of the workpiece and the temperature decreases with increasing welding time and distance, this is due to the cooling process of the material as a result of conduction to the backing plate and room temperature [25].…”

Section: Scanning Electron Microscopy (Sem)mentioning

confidence: 99%

Morphology Study of the Corrosion Rate on Weld Joint of Double Side Friction Stir Welding Aluminum Alloy AA6061

Crisdion

Puspitasari

Permanasari

et al. 2023

jtmt

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DSFSW welding uses the rotating of a tool in the workpiece to create heat owing to friction between workpiece and tool to be connected. Corrosion is influenced by the presence of high heat owing to friction and a non-uniform cooling rate. The heat transforms the metal in the Heat Affected Zone (HAZ) and Weld Metal (WM) region, which can result in flaws such porosity, kissing bond, fractures, voids (from welding penetration), and flash as well as changes to the microstructure. Changes in the microstructure of welded joints can affect the resistance of welded joints to corrosion. The purpose of this study is to experimentally analyze the main causes of failure of AA6061 DSFSW joints based on welding temperature, weld defects, microstructure, corrosion rate and morphology of the corroded surface. Temperature testing using thermocouple to analyze the temperature, welding joint defects using DSLR camera and radiography test, microstructure using optical microscope, corrosion rate using AUTOLAB PGSTAT and morphology of corroded surface using SEM. The temperature analysis results show that the advancing side has a higher temperature than the retreating side, due to friction between the tool and base metal accompanied by the opposite welding direction. Visual inspection shows that all specimens and welding positions produce flash that is quite rough on the top (1G) and bottom (4G) surfaces and radiographic test results show incomplete fusion in 4 specimens. Microstructure shows a change in shape and size resulting in recrystallization in the form of fine grains. The highest corrosion rate is found in specimen B 1G welding position of 0.63856mm/year and the lowest corrosion rate in specimen A of 0.058567mm/year. SEM test results show the type of corrosion that occurs in DSFSW welding joints is pitting corrosion.

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Temperature and Stress Evaluation during Friction Stir Welding of Inconel 718 Alloy Using Finite Element Numerical Simulation

Cited by 16 publications

References 20 publications

Analyzing Welding Performance of Metal Using Artificial Neural Network

Analyzing Welding Performance of Metal Using Artificial Neural Network

FEM-Based Thermogram Correction for Inconel 625 Joint Hardness Clustering

Morphology Study of the Corrosion Rate on Weld Joint of Double Side Friction Stir Welding Aluminum Alloy AA6061

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