Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals

Nunes, Thiago; Albuquerque, Victor Hugo C. de; Papa, João Paulo; Silva, Cleiton Carvalho; Normando, Paulo G.; Moura, Elineudo Pinho de; Tavares, João Manuel R. S.

doi:10.1016/j.eswa.2012.12.025

Cited by 39 publications

(25 citation statements)

References 25 publications

(30 reference statements)

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“…Therefore, techniques to monitor the embrittlement of stainless steels need to be precise in this range of the sigma phase content. However, as previously mentioned, other non-destructive testing techniques, like eddy current and ultrasound, are not so effective in this range [6,27]. The precipitation of sigma phase, which is a paramagnetic phase, reduces the magnetic permeability of the steel as ferrite phase is ferromagnetic and decomposes into it.…”

Section: Resultsmentioning

confidence: 99%

Classification of Induced Magnetic Field Signals for the Microstructural Characterization of Sigma Phase in Duplex Stainless Steels

Silva

Marinho

Filho

et al. 2016

Metals

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Duplex stainless steels present excellent mechanical and corrosion resistance properties. However, when heat treated at temperatures above 600 • C, the undesirable tertiary sigma phase is formed. This phase presents high hardness, around 900 HV, and it is rich in chromium, the material toughness being compromised when the amount of this phase is not less than 4%. This work aimed to develop a solution for the detection of this phase in duplex stainless steels through the computational classification of induced magnetic field signals. The proposed solution is based on an Optimum Path Forest classifier, which was revealed to be more robust and effective than Bayes, Artificial Neural Network and Support Vector Machine based classifiers. The induced magnetic field was produced by the interaction between an applied external field and the microstructure. Samples of the 2205 duplex stainless steel were thermal aged in order to obtain different amounts of sigma phases (up to 18% in content). The obtained classification results were compared against the ones obtained by Charpy impact energy test, amount of sigma phase, and analysis of the fracture surface by scanning electron microscopy and X-ray diffraction. The proposed solution achieved a classification accuracy superior to 95% and was revealed to be robust to signal noise, being therefore a valid testing tool to be used in this domain.

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Section: Resultsmentioning

confidence: 99%

Classification of Induced Magnetic Field Signals for the Microstructural Characterization of Sigma Phase in Duplex Stainless Steels

Silva

Marinho

Filho

et al. 2016

Metals

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“…The commonly used behavior assessment models in engineering include Bayes assessment model [23] and G1 behavior sequence model [22], and so forth. Bayes assessment model belongs to the mean-variance assessment method, and G1 behavior sequence model belongs to the expert assessment method.…”

Section: Behavioral Assessment Systemmentioning

confidence: 99%

Dynamic behavioral assessment model based on Hebb learning rule

Yin

Yuan

Zhang

2016

Neural Comput & Applic

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Behavioral assessment based on computing system is with important value for computer-simulated training and system diagnosis. However, the existing assessment is a static method for ex post evaluation, and the low efficiency and high complexity have been the urgent problems to be solved in the academic field. In this paper, we propose an adaptive dynamic behavioral assessment model based on Hebb learning rule that effectively combines the assessment standard and the weights of factors. The dynamic behavioral assessment considers the relative weights between the assessment indexes, whereas the existing assessment method does not; the dynamic behavioral assessment uses the assessment standard data recursively and can conduct an instant assessment for the objectives. We have built an assessment system for computer-simulated training, and took the pilot behavioral assessment for example to test and verify the dynamic behavioral assessment mode. Experimental results show that the dynamic behavioral assessment model based on Hebb learning rule has more advantage in assessment efficiency and online computing support.

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“…storage modulus, loss modulus, and tan delta) with the minimal mean square error [8]. Nunes et al [9] evaluated the efficiency and accuracy of artificial intelligence techniques to classify ultrasound signals, raw data and feature selection methods, background echo and backscattered signals acquired at frequencies of 4 and 5 MHz to characterize the microstructural kinetics of phase transformations on a Nb-base alloy, thermally aged at 650 and 950°C for 10, 100 and 200 h. Papa et al [10] implemented SVMs, Bayesian and Optimum-Path Forest (OPF) based classifiers, and also the Otsu's method for automatic characterization of particles in metallographic images. De Albuquerque et al [11] presented an ANN model to automatically segment and quantify material phases from SEM metallographic images and then the results were compared to a commercial software used for quantifying material phases from metallographic images.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive mechanical property classification of vapor-grown carbon nanofiber/vinyl ester nanocomposites using support vector machines

Abuomar¹,

Nouranian²,

King

et al. 2015

Computational Materials Science

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Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals

Cited by 39 publications

References 25 publications

Classification of Induced Magnetic Field Signals for the Microstructural Characterization of Sigma Phase in Duplex Stainless Steels

Classification of Induced Magnetic Field Signals for the Microstructural Characterization of Sigma Phase in Duplex Stainless Steels

Dynamic behavioral assessment model based on Hebb learning rule

Comprehensive mechanical property classification of vapor-grown carbon nanofiber/vinyl ester nanocomposites using support vector machines

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