首页|New Findings on Machine Learning from Hebei University Summarized (Damage Recognition of Acoustic Emission and Micro-ct Characterization of Bi-adhesive Repaired Composites Based On the Machine Learning Method)
New Findings on Machine Learning from Hebei University Summarized (Damage Recognition of Acoustic Emission and Micro-ct Characterization of Bi-adhesive Repaired Composites Based On the Machine Learning Method)
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NETL
NSTL
Springer Nature
Investigators publish new report on Machine Learning. According to news reporting from Baoding, People’s Republic of China, by NewsRx journalists, research stated, “Bi-adhesive repair method is one of several repair technologies that use the adhesive bonding approach for patch-repaired composites. However, these repairs are subject to matrix-cracking and interface debonding damage.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC), Innovation Team of Nondestructive Testing Technology, Hebei University. The news correspondents obtained a quote from the research from Hebei University, “Furthermore, a change in the length ratio (the length of the rigid adhesive region divided by the length of the overall repaired region) also produces a change in the damage modes, which has a significant impact on the repair performance. Hence, this study aims to evaluate the effects of four different length ratios (0, 0.2, 0.5, 1) on the behavior of damage evolution in bi-adhesive repaired composites. The acoustic emission damage identification and micro-CT characterization are carried out based on the machine learning method. A simple prediction method is employed to distinguish damage modes in bi-adhesive repaired composites, achieving a prediction accuracy over 90%. The results demonstrated that the length ratio has a substantial effect on matrix-cracking, fiber-matrix debonding, and their interaction in bi-adhesive repaired composites. These acquired characteristics information of acoustic emission signals provide insights into the impact of length ratio on the progression of damage evolution. Additionally, the visualization of interior damage offers insights into the variations in failure characteristics within distinct bi-adhesive repaired composites, thereby supporting the conclusions gained from acoustic emission studies.”
BaodingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningHebei University
NETL
NSTL
Springer Nature
