Paper
18 April 2023 Sensitivity analysis of the ultrasound features for the sensitization prediction of Al-Mg alloys via Adaptive Neuro-Fuzzy Inference System (ANFIS)
M. Rahimi Dizadji, A. Abusoua, H. Huang
Author Affiliations +
Abstract
This research analyzes the sensitivity of the ultrasound features to sensitization, a microstructural damage developed in Al-Mg alloys when subjected to extended period of heat treatment. For rule extraction, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was first developed and customized to house the ultrasound features from the Electromechanical Impedance (EMI) of Piezoelectric Wafer Active Sensor (PWAS) attached on aluminum plates with progressive sensitization development. The features are rooted in the rise time and the amplitude of the time domain signal that was obtained at two different resonance phases. The extracted fuzzy rules were analyzed to determine the sensitivity of each feature at both the model and rule level. The analysis revealed that the rise time changes at higher resonance frequency and the amplitude change at lower resonance frequency has a stronger correlation to sensitization. The results of rule significance evaluation demonstrated that the data measured at low resonance frequency exhibits more correlation to sensitization.
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M. Rahimi Dizadji, A. Abusoua, and H. Huang "Sensitivity analysis of the ultrasound features for the sensitization prediction of Al-Mg alloys via Adaptive Neuro-Fuzzy Inference System (ANFIS)", Proc. SPIE 12486, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023, 124860R (18 April 2023); https://doi.org/10.1117/12.2654848
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KEYWORDS
Ultrasonography

Alloys

Amplifiers

Heat treatments

Education and training

Aluminum

Fuzzy logic

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