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    Magnetic Barkhausen Noise Technique for Fatigue Detection and Classification in Martensitic StainlessSteel

    Source: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems:;2022:;volume( 005 ):;issue: 004::page 41010
    Author:
    Basti Shenoy, Bharath;Li, Zi;Udpa, Lalita;Udpa, Satish;Deng, Yiming;SeuaciucOsorio, Thiago
    DOI: 10.1115/1.4055992
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Stainless steel is used in many applications because of its excellent mechanical properties at elevated temperatures. Material fatigue is a major problem in steel structures and can cause catastrophic damage resulting in significant economic consequences. Conventional nondestructive evaluation techniques can detect macro defects but do not perform well when it comes to material degradation due to fatigue, which happens at a microstructure level. It is well known that stress applied on a material will have an impact on the microstructure and produces a change in the magnetic properties of the material. Hence, magnetic nondestructive evaluation techniques that are sensitive to changes in magnetic properties play a major role in the earlystage fatigue detection, i.e., before the macro crack initiates. This paper introduces the magnetic Barkhausen noise technique to garner information about fatigue state of the material under test. Kmedoids clustering algorithm and genetic optimization algorithm are used to classify the stainlesssamples into fatigue categories. The results prove that martensitic grade stainlesssteel samples in different stages of fatigue can be classified into broad fatigue categories, i.e., low fatigue, mid fatigue, and high fatigue based on the remaining useful life of the sample.
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      Magnetic Barkhausen Noise Technique for Fatigue Detection and Classification in Martensitic StainlessSteel

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    contributor authorBasti Shenoy, Bharath;Li, Zi;Udpa, Lalita;Udpa, Satish;Deng, Yiming;SeuaciucOsorio, Thiago
    date accessioned2023-04-06T12:59:08Z
    date available2023-04-06T12:59:08Z
    date copyright11/8/2022 12:00:00 AM
    date issued2022
    identifier issn25723901
    identifier othernde_5_4_041010.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4288877
    description abstractStainless steel is used in many applications because of its excellent mechanical properties at elevated temperatures. Material fatigue is a major problem in steel structures and can cause catastrophic damage resulting in significant economic consequences. Conventional nondestructive evaluation techniques can detect macro defects but do not perform well when it comes to material degradation due to fatigue, which happens at a microstructure level. It is well known that stress applied on a material will have an impact on the microstructure and produces a change in the magnetic properties of the material. Hence, magnetic nondestructive evaluation techniques that are sensitive to changes in magnetic properties play a major role in the earlystage fatigue detection, i.e., before the macro crack initiates. This paper introduces the magnetic Barkhausen noise technique to garner information about fatigue state of the material under test. Kmedoids clustering algorithm and genetic optimization algorithm are used to classify the stainlesssamples into fatigue categories. The results prove that martensitic grade stainlesssteel samples in different stages of fatigue can be classified into broad fatigue categories, i.e., low fatigue, mid fatigue, and high fatigue based on the remaining useful life of the sample.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleMagnetic Barkhausen Noise Technique for Fatigue Detection and Classification in Martensitic StainlessSteel
    typeJournal Paper
    journal volume5
    journal issue4
    journal titleJournal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
    identifier doi10.1115/1.4055992
    journal fristpage41010
    journal lastpage410105
    page5
    treeJournal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems:;2022:;volume( 005 ):;issue: 004
    contenttypeFulltext
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