Transition of interfacial friction regime and its influence on thermal responses in rotary friction welding of SUS304 stainless steel: A fully coupled transient thermomechanical analysis

Tang, Tianxiang; Shi, Qingyu; Lei, Bingwang; Zhou, Jun; Gao, Yunxi; Li, Yongqing; Zhang, Gong; Chen, Gaoqiang

doi:10.1016/j.jmapro.2022.08.016

Cited by 11 publications

(3 citation statements)

References 48 publications

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“…In the RFW process, the accurate measurement of the temperature during experimental tests can be challenging. To overcome this, numerical simulation of the RFW process has been introduced as the most effective method for studying the temperature evolution [47,49]. It is known that 90% of heat generation in this process is attributed to the friction between two surfaces [20].…”

Section: Thermal-mechanical Simulation Resultsmentioning

confidence: 99%

Thermal–Mechanical and Microstructural Simulation of Rotary Friction Welding Processes by Using Finite Element Method

Mani,

Taherizadeh,

Sadeghian

et al. 2024

Materials

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Rotary friction welding is one of the most crucial techniques for joining different parts in advanced industries. Experimentally measuring the history of thermomechanical and microstructural parameters of this process can be a significant challenge and incurs high costs. To address these challenges, the finite element method was used to simulate thermomechanical and microstructural aspects of the welding of identical superalloy Inconel 718 tubes. Numerical simulation results were used to compute essential mechanical and metallurgical parameters such as temperature, strain, strain rate, volume fraction of dynamic recrystallization, and grain size distribution. These parameters were subsequently verified using experimental test results. The Johnson–Avrami model was utilized in the microstructural simulation to convert thermomechanical parameters into metallurgical factors, employing a FORTRAN subroutine. The calculated thickness of the recrystallization zone in the wall was 480 and 850 μm at the tube wall’s center and edge, respectively. These values were reported from experimental measurements as 500 and 800 μm, respectively. The predicted grain size changes from the center to the edge of the wall thickness, near the weld interface, ranged from 2.07 to 2.15 μm, comparable to the experimental measurements ranging from 1.9 to 2.2 μm. Various curves are also presented to explore the correlation between thermomechanical and microstructural parameters, with the experimental results revealing predictable microstructure evolutions correlated with thermomechanical changes.

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Section: Thermal-mechanical Simulation Resultsmentioning

confidence: 99%

Thermal–Mechanical and Microstructural Simulation of Rotary Friction Welding Processes by Using Finite Element Method

Mani,

Taherizadeh,

Sadeghian

et al. 2024

Materials

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“…The µ that only related to sliding velocity was also used in the numerical simulation [39]. Recently, the friction coefficient related to temperature, pressure, and sliding velocity was applied in the numerical calculation of the welding process [40][41][42][43][44][45].…”

Section: Heat Input and Friction Coefficientmentioning

confidence: 99%

Experimental and FEM Studies of Continuous Drive Friction Welding of Ferritic Spheroidal Graphite Cast Iron

Winiczenko,

Skibicki

2024

Processes

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Experimental and FEM studies of the friction welding process of spheroidal graphite cast iron (SGCI) are presented. A coupled thermal and mechanical 2.5 D FEM model was used to simulate the continuous drive friction welding (CDFW) process. The FE model predicted the peak temperature of the joint, effective stress, axial shortening, and the weld flash size. Additionally, the friction force on the axial shortening of specimens was studied. The peak temperatures were measured both on the axis and at the surface of the specimen. The predicted maximum temperatures in the axis, ½ radius, and 2 mm from the surface of the sample amounted to 1162 °C, 1177 °C, and 1061 °C, respectively. The maximum temperature of the spheroidal graphite cast iron joint was below the melting temperature of the base material (~1350 °C). The predicted temperature curves, outbursts, and shortening of welded elements indicated a good match with real models.

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“…Certain manufacturing processes at high temperatures [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ] of In718 alloy are generally involved to maintain its mechanical properties and the geometric part. In previous studies [ 14 , 15 ], finite element simulation was employed to predict and visualize the in-process thermal and mechanical variables during these manufacturing processes. With this information, the optimization of processing parameters could be expedited with more confidence, as both the thermal and mechanical variables are relevant to shape the final microstructure and thus the properties.…”

Section: Introductionmentioning

confidence: 99%

Towards an Optimized Artificial Neural Network for Predicting Flow Stress of In718 Alloys at High Temperatures

et al. 2023

Self Cite

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Artificial neural networks (ANNs) have been an important approach for predicting the value of flow stress, which is dependent on temperature, strain, and strain rate. However, there is still a lack of sufficient knowledge regarding what structure of ANN should be used for predicting metal flow stress. In this paper, we train an ANN for predicting flow stress of In718 alloys at high temperatures using our experimental data, and the structure of the ANN is optimized by comparing the performance of four ANNs in predicting the flow stress of In718 alloy. It is found that, as the size of the ANN increases, the ability of the ANN to retrieve the flow stress results from a training dataset is significantly enhanced; however, the ability to predict the flow stress results absent from the training does not monotonically increase with the size of the ANN. It is concluded that the ANN with one hidden layer and four nodes possesses optimized performance for predicting the flow stress of In718 alloys in this study. The reason why there exists an optimized ANN size is discussed. When the ANN size is less than the optimized size, the prediction, especially the strain dependency, falls into underfitting and fails to predict the curve. When the ANN size is less than the optimized size, the predicted flow stress curves with the temperature, strain, and strain rate will contain non-physical fluctuations, thus reducing their prediction accuracy of extrapolation. For metals similar to the In718 alloy, ANNs with very few nodes in the hidden layer are preferred rather than the large ANNs with tens or hundreds of nodes in the hidden layers.

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Transition of interfacial friction regime and its influence on thermal responses in rotary friction welding of SUS304 stainless steel: A fully coupled transient thermomechanical analysis

Cited by 11 publications

References 48 publications

Thermal–Mechanical and Microstructural Simulation of Rotary Friction Welding Processes by Using Finite Element Method

Thermal–Mechanical and Microstructural Simulation of Rotary Friction Welding Processes by Using Finite Element Method

Experimental and FEM Studies of Continuous Drive Friction Welding of Ferritic Spheroidal Graphite Cast Iron

Towards an Optimized Artificial Neural Network for Predicting Flow Stress of In718 Alloys at High Temperatures

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