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

N. Amiri; G. H. Farrahi; K. Reza Kashyzadeh; M. Chizari

doi:10.1016/j.jmapro.2020.01.047

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

N. Amiri, G. H. Farrahi, K. Reza Kashyzadeh, M. Chizari

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

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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.

Original language	English
Pages (from-to)	26-34
Number of pages	9
Journal	Journal of Manufacturing Processes
Volume	52
Early online date	31 Jan 2020
DOIs	https://doi.org/10.1016/j.jmapro.2020.01.047
Publication status	Published - Apr 2020

Keywords

Spot welding joint of multiple sheets; Ultrasonic test; Image processing Static strength; Fatigue life; Artificial neural network; Genetic algorithm
RSW joint of multiple sheets
Ultrasonic test
Fatigue behavior
Image processing
Genetic algorithm
Artificial neural network
Static strength

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10.1016/j.jmapro.2020.01.047Licence: Other

Revised_Machine_Learning_Method_UT
© 2020 The Society of Manufacturing Engineers. This manuscript is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0). For further details please see: https://creativecommons.org/licenses/by-nc-nd/4.0/
Accepted author manuscript, 1 MBLicence: CC BY-NC-ND

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

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.",

keywords = "Spot welding joint of multiple sheets; Ultrasonic test; Image processing Static strength; Fatigue life; Artificial neural network; Genetic algorithm, RSW joint of multiple sheets, Ultrasonic test, Fatigue behavior, Image processing, Genetic algorithm, Artificial neural network, Static strength",

author = "N. Amiri and Farrahi, {G. H.} and {Reza Kashyzadeh}, K. and M. Chizari",

note = "{\textcopyright} 2020 The Society of Manufacturing Engineers. This manuscript is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0). For further details please see: https://creativecommons.org/licenses/by-nc-nd/4.0/",

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doi = "10.1016/j.jmapro.2020.01.047",

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AU - Amiri, N.

AU - Farrahi, G. H.

AU - Reza Kashyzadeh, K.

AU - Chizari, M.

N1 - © 2020 The Society of Manufacturing Engineers. This manuscript is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0). For further details please see: https://creativecommons.org/licenses/by-nc-nd/4.0/

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N2 - 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.

AB - 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.

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KW - RSW joint of multiple sheets

KW - Ultrasonic test

KW - Fatigue behavior

KW - Image processing

KW - Genetic algorithm

KW - Artificial neural network

KW - Static strength

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JO - Journal of Manufacturing Processes

JF - Journal of Manufacturing Processes

ER -

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

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