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        Applications of ultrasonic testing and machine learning methods to predict the static & fatigue behavior of spot-welded joints

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        Revised_Machine_Learning_Method_UT.pdf (PDF, 1020Kb)(embargoed until 31/01/2021)
        Author
        Amiri, N.
        Farrahi, G. H.
        Reza Kashyzadeh, K.
        Chizari, M.
        Attention
        2299/22206
        Abstract
        Ultrasonic Testing (UT) is one of the well-known Non-Destructive Techniques (NDT) of spot-weld inspection in the advanced industries, especially in automotive industry. However, the relationship between the UT results and strength of the spot-welded joints subjected to various loading conditions isunknown. The main purpose of this research is to present an integrated search system as a new approach for assessment of tensile strength and fatigue behavior of the spot-welded joints. To this end, Resistance Spot Weld (RSW) specimens of three-sheets were made of different types of low carbon steel. Afterward, the ultrasonic tests were carried out and the pulse-echo data of each sample were extracted utilizing Image Processing Technique (IPT). Several experiments (tensile and axial fatigue tests) were performed to study the mechanical properties of RSW joints of multiple sheets. The novel approach of the present research is to provide a new methodology for static strength and fatigue life assessment of three-sheets RSW joints based on the UT results by utilizing Artificial Neural Network (ANN) simulation. Next, Genetic Algorithm (GA) was used to optimize the structure of ANN. This approach helps to decrease the number of tests and the cost of performing destructive tests with appropriate reliability.
        Publication date
        2020-04
        Published in
        Journal of Manufacturing Processes
        Published version
        https://doi.org/10.1016/j.jmapro.2020.01.047
        Other links
        http://hdl.handle.net/2299/22206
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