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    A Semi-Analytical Nonlinear Regression Approach for Weld Profile Prediction: A Case of Alternating Current Square Waveform Submerged Arc Welding of Heat Resistant Steel

    Source: Journal of Manufacturing Science and Engineering:;2018:;volume( 140 ):;issue: 011::page 111013
    Author:
    Mohanty, Uttam Kumar
    ,
    Sharma, Abhay
    ,
    Nakatani, Mitsuyoshi
    ,
    Kitagawa, Akikazu
    ,
    Tanaka, Manabu
    ,
    Suga, Tetsuo
    DOI: 10.1115/1.4040983
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The complexity in weld profile caused by abrupt change in polarity in square waveform welding is investigated through the development of a model capable to accurately predict weld profile. A semi-analytical model is conceived wherein characteristic attributes of a composite parabolic–elliptic function, which represent the weld profile, are obtained through nonlinear regression (NLR). The proposed model is demonstrated for its efficacy in the prediction of weld profile over a wide range of welding parameters, vis-à-vis, welding current, frequency, electrode negative (EN) ratio, and welding velocity. The investigation suggests that the center and outer cores of welding arc remains more active during positive and negative polarity, respectively, that leads to distinct macroscopic zones in weld cross section and thus, necessitates a composite profile for representation of weld profile. The intersection of the zones forms a metallurgical notch which the investigation offers a method to estimate and thus control. Unlike the convention continuous arc welding, the waveform arc welding caters welding at higher velocity without compromising the weld penetration and almost abolishing the metallurgical notch as well.
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      A Semi-Analytical Nonlinear Regression Approach for Weld Profile Prediction: A Case of Alternating Current Square Waveform Submerged Arc Welding of Heat Resistant Steel

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    contributor authorMohanty, Uttam Kumar
    contributor authorSharma, Abhay
    contributor authorNakatani, Mitsuyoshi
    contributor authorKitagawa, Akikazu
    contributor authorTanaka, Manabu
    contributor authorSuga, Tetsuo
    date accessioned2019-02-28T11:02:39Z
    date available2019-02-28T11:02:39Z
    date copyright8/31/2018 12:00:00 AM
    date issued2018
    identifier issn1087-1357
    identifier othermanu_140_11_111013.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4252041
    description abstractThe complexity in weld profile caused by abrupt change in polarity in square waveform welding is investigated through the development of a model capable to accurately predict weld profile. A semi-analytical model is conceived wherein characteristic attributes of a composite parabolic–elliptic function, which represent the weld profile, are obtained through nonlinear regression (NLR). The proposed model is demonstrated for its efficacy in the prediction of weld profile over a wide range of welding parameters, vis-à-vis, welding current, frequency, electrode negative (EN) ratio, and welding velocity. The investigation suggests that the center and outer cores of welding arc remains more active during positive and negative polarity, respectively, that leads to distinct macroscopic zones in weld cross section and thus, necessitates a composite profile for representation of weld profile. The intersection of the zones forms a metallurgical notch which the investigation offers a method to estimate and thus control. Unlike the convention continuous arc welding, the waveform arc welding caters welding at higher velocity without compromising the weld penetration and almost abolishing the metallurgical notch as well.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Semi-Analytical Nonlinear Regression Approach for Weld Profile Prediction: A Case of Alternating Current Square Waveform Submerged Arc Welding of Heat Resistant Steel
    typeJournal Paper
    journal volume140
    journal issue11
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4040983
    journal fristpage111013
    journal lastpage111013-11
    treeJournal of Manufacturing Science and Engineering:;2018:;volume( 140 ):;issue: 011
    contenttypeFulltext
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