Artificial Neural Network Model for Estimation Effect of Shot Peening Time on Fatigue Life of Aluminum Alloy (2024-T3) Welded


Sulaiman Al-basaqr, Amir Alsammarraie, Adel mahmood bash, Mohammed S. Albayati


University of Tikrit/ Engineering College/ Mechanical Engineering Dep. Tikrit, Iraq

Corresponding Author Email: sulaimaninad63@tu.edu.iq

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Improving fatigue life is the most challenging engineering problem due to the high stresses generated during the welding process, Surface treatment using shot peening steel ball is one of the methods of converting harmful welding stresses into useful compressive stresses. The purpose of this study is to predict the fatigue life of aluminum alloys (2024-T3) welded and shot peening steel ball with varying times using ANN method. The fatigue life was tested for aluminum alloy (2024-T3) with a thickness of (2.5 mm), the welding process was performed on one side by means of tungsten welding (TIG). The welding area was subjected to the shot peening streel ball process with different blasting times (15,10,5) minutes. Fatigue tests were performed by carrying the bending inside the plane with R = ± 1 and S-N curves being plotted, impact load was fixed for all examined samples at 8, 10, 12 and 15 N. It applies ANN as a model approach for estimating the life characteristics of fatigue and comparing it with the experimental results. The results showed an improvement in the fatigue life of aluminum alloy