Detection of crack development with Al/SiCp using tensile with online acoustic emission

Christopher, C; Sasikumar, T.; Suresh, S.

doi:10.1016/j.jallcom.2018.11.162

Cited by 9 publications

(1 citation statement)

References 32 publications

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“…[ 17 ] Additionally, changes in the microstructure such as dislocation, twinning, and cracks influence the variations in the characteristic parameters of AE signals, which can be used to predict the material's strength. [ 18,19 ] Different plastic deformation mechanisms produce AE signals with distinct characteristics. For example, AE signals from dislocation slip are continuous, while those from twinning are more abrupt.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Online Sensing of Defects Evolution in Metallic Materials during Plastic Deformation

Tang,

He,

Guo

et al. 2023

Advanced Intelligent Systems

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Metallic plastic deformation involves complex microstructural changes and defect evolution, posing challenges in predicting and controlling the quality and performance of formed parts. Therefore, a pressing demand exists for a proficient online defect‐sensing system to monitor defects evolution continuously within components during plastic deformation in real time. This article proposes an intelligent online sensing approach for detecting defects in metallic plastic forming based on acoustic emission (AE) and machine learning. A comparative analysis is conducted on AE amplitude signals, stress–strain curves, and defect evolution during the tensile process of TA15 titanium alloy specimens under different stress states. It is found that the defect formation process can be divided into four stages based on the AE amplitude signals. A convolutional neural network model for intelligent defect sensing is established. It leverages transfer learning and is grounded in the relationship between AE signals and the evolution of internal defects. The prediction accuracy using different pretrained models is investigated and compared. It is discerned that utilizing GoogleNet as the pretrained model offers the swiftest training pace with a prediction accuracy of 97.57%. This approach enables intelligent online sensing of internal defect evolution in metal plastic deformation processes.

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Section: Introductionmentioning

confidence: 99%