Fatigue life assessment of carbon nanotubes reinforced Al-Cu-Mg composite foams via statistics analysis and computer vision

The unsatisfactory fatigue performance of Al foams significantly limits their engineering applications. Meanwhile, the unique pore structure of metal foams makes it still challenging to assess its fatigue. Herein, we report an innovative strategy for preparing carbon nanotubes reinforced Al-Cu-Mg (CNTs/Al-Cu-Mg) composite foams, which exhibit superior compressive strength (16.17 MPa) and fatigue strength (11.17 MPa). Statistics analysis confirms that the fatigue life ( N f ) follows Weibull distribution, and P-S-N curves were obtained to calculate the theoretical stress level required to achieve the expected N f . Moreover, computer vision is utilized to monitor the fatigue deformation process and effectively detect fatigue crack initiation sites. Notably, the accuracy of predicted N f obtained by this method is exceptionally high (98.32%). This work provides a theoretical and practical basis for designing metal foams with superior fatigue properties and offers a promising bottom-up tactic for predicting the N f of metal foams to prevent fatigue failure. Highlights The CNTs/Al-Cu-Mg composite foams possess the superior fatigue strength of 11.17 MPa. The application of computer vision enables the accurate monitoring of FCIS. A novel exploration into the fatigue assessment of metal foams is developed.