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Statistical analysis of slip transfer in Al alloy based on in-situ tensile test and high-throughput computing method
•Slip transfer phenomenon in Al-Mg alloy is studied by high-throughput computing.•Local lattice rotation is considered in slip transfer analysis for the first time.•Two slip parameters are proposed based on a statistical analysis of 155,250 slip pairs.•The new parameters can effectively improve the...
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Published in: | International journal of plasticity 2023-07, Vol.166, p.103649, Article 103649 |
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Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | •Slip transfer phenomenon in Al-Mg alloy is studied by high-throughput computing.•Local lattice rotation is considered in slip transfer analysis for the first time.•Two slip parameters are proposed based on a statistical analysis of 155,250 slip pairs.•The new parameters can effectively improve the prediction accuracy of slip transfer.
Slip transfer mechanism is studied based on in-situ tensile tests performed on an Al-Mg alloy and high-throughput computing. A statistical analysis of slip transfer is performed for over 1180 grain boundaries and 155,250 slip pairs while considering the local lattice rotation, grain boundaries, and slip system geometries. Two new slip transfer parameters, N and B, representing the alignment of the slip planes and the activation of slip systems in adjacent grains, are proposed. A decision tree model is built to evaluate the prediction accuracy of the slip transfer parameters. The optimized results of the decision tree classifiers show that the two new parameters are more effective than the Luster–Morris parameter, m′. The improvement in the classification accuracy is attributable to the information regarding the slip plane geometry and orientation evolution contained in N and B. The new slip transfer parameters improve our understanding of the slip transfer mechanism and can be integrated into plasticity models to predict the deformation and fracture behaviors of polycrystalline materials.
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ISSN: | 0749-6419 1879-2154 |
DOI: | 10.1016/j.ijplas.2023.103649 |