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 journalArticlepeer-review

12 Citations (Scopus)
89 Downloads (Pure)


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 languageEnglish
Pages (from-to)26-34
Number of pages9
JournalJournal of Manufacturing Processes
Early online date31 Jan 2020
Publication statusPublished - Apr 2020


  • 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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