Influence of accelerated corrosion on Al/steel RSW joints by in situ compression tests

Pan, Bo; Sun, Hui; Xie, Dongyue; Shang, Shun-Li; Li, Nan; Carlson, Blair E.; Li, Yumeng; Liu, Zi-Kui; Li, Jingjing

doi:10.1016/j.msea.2023.145851

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…With the improvement of social demand and the development of modern industry, some new types of HSLA steels have been developed by means of reasonable chemical composition design, advanced material preparation, and modification processes [4,5]. Some computational materials science methods and tools have played a crucial role in composition design and process optimization and have been widely used in the research and development process of materials [6][7][8]. After the proposal of ICME and MGI [9,10], high-throughput material calculations, high-throughput experiments, and big data technology have received widespread attention, which has promoted the development of materials science and engineering [11], including research on material hot deformation behavior based on machine learning technology [12,13].…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Flow Stress in the Hot Compression of a Ni-Cr-Mo Steel Using Machine Learning Algorithms

Pan,

Song,

Gao

et al. 2024

Processes

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The constitutive model refers to the mapping relationship between the stress and deformation conditions (such as strain, strain rate, and temperature) after being loaded. In this work, the hot deformation behavior of a Ni-Cr-Mo steel was investigated by conducting isothermal compression tests using a Gleeble-3800 thermal simulator with deformation temperatures ranging from 800 °C to 1200 °C, strain rates ranging from 0.01 s−1 to 10 s−1, and deformations of 55%. To analyze the constitutive relation of the Ni-Cr-Mo steel at high temperatures, five machine learning algorithms were employed to predict the flow stress, namely, back-propagation artificial neural network (BP-ANN), Random Committee, Bagging, k-nearest neighbor (k-NN), and a library for support vector machines (libSVM). A comparative study between the experimental and the predicted results was performed. The results show that correlation coefficient (R), root mean square error (RMSE), mean absolute value error (MAE), mean square error (MSE), and average absolute relative error (AARE) obtained from the Random Committee on the testing set are 0.98897, 8.00808 MPa, 5.54244 MPa, 64.12927 MPa2 and 5.67135%, respectively, whereas the metrics obtained via other algorithms are all inferior to the Random Committee. It suggests that the Random Committee can predict the flow stress of the steel more effectively.

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Section: Introductionmentioning

confidence: 99%

The Prediction of Flow Stress in the Hot Compression of a Ni-Cr-Mo Steel Using Machine Learning Algorithms

Pan,

Song,

Gao

et al. 2024

Processes

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Microstructure, Corrosion and Mechanical Properties of Medium-Thick 6061-T6 Alloy/T2 Pure Cu Dissimilar Joints Produced by Double Side Friction Stir Z Shape Lap-Butt Welding

Tang,

Dai,

Shi

et al. 2024

Chin. J. Mech. Eng.

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A novel double side friction stir Z shape lap-butt welding (DS-FSZW) process was proposed to achieve excellent mechanical properties of Al/Cu medium-thick dissimilar joints. The influence of welding parameters on weld microstructure and properties of DS-FSZW joint were systematically investigated. It indicated that defect-free medium-thick Al/Cu DS-FSZW joint could be achieved under an optimal welding parameter. DS-FSZW joint was prone to form void defects in the bottom of the second-pass weld. The recrystallization mechanisms at the top and middle of the weld nugget zone (WNZ) were continuous dynamic recrystallization (CDRX) and geometric dynamic recrystallization (GDRX). While the major recrystallization mechanism at the bottom of the WNZ was GDRX. DS-FSZW joint of the optimal welding condition with 850 r/min-400 mm/min was produced with a continuous thin and crack-free IMCs layer at the Al/Cu interface, and the maximum tensile strength of this joint is 160.57 MPa, which is equivalent to 65.54% of pure Cu base material. Moreover, the corrosion resistance of Al/Cu DS-FSZW joints also achieved its maximum value at the optimal welding parameter of 850 r/min-400 mm/min. It demonstrates that the DS-FSZW process can simultaneously produce medium-thick Al/Cu joints with excellent mechanical performance and corrosion resistance.

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Influence of accelerated corrosion on Al/steel RSW joints by in situ compression tests

Cited by 2 publications

References 55 publications

The Prediction of Flow Stress in the Hot Compression of a Ni-Cr-Mo Steel Using Machine Learning Algorithms

The Prediction of Flow Stress in the Hot Compression of a Ni-Cr-Mo Steel Using Machine Learning Algorithms

Microstructure, Corrosion and Mechanical Properties of Medium-Thick 6061-T6 Alloy/T2 Pure Cu Dissimilar Joints Produced by Double Side Friction Stir Z Shape Lap-Butt Welding

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