On the Crush Behavior and Energy Absorption of Sustainable Beverage Cans and Their Polyurethane Foam-Filled Structures: An Experimental Study.

In the pursuit of global energy conservation and emissions reductions, utilizing beverage cans as energy-absorbing components offers potential for a sustainable economy. This study examines the impact of foam filling on the crushing behaviors and energy absorption of various types of beverage cans. Quasi-static compression tests were conducted on five geometrically sized cans filled with three densities of polyurethane foam to study their deformation modes and calculate crashworthiness parameters within the effective stroke. Results show that empty beverage cans have lower energy absorption capacities, and deformation modes become less consistent as can size increases. Higher foam density leads to increased total energy absorption, a slight reduction in the effective compression stroke, and a tendency for specific energy absorption to initially increase and then decrease. Regarding crush behavior, smaller cans transition from a diamond mode to a concertina mode, while larger cans exhibit a columnar bending mode. Next, the coupling effect of energy absorption between foam and cans was analyzed so as to reveal the design method of energy-absorbing components. The specific energy absorption of smaller cans filled with polyurethane foam is superior to that of similar empty cans. These findings provide valuable insights for selecting next-generation sustainable energy absorption structures.

Low-Velocity Impact Behavior of Sandwich Plates with FG-CNTRC Face Sheets and Negative Poisson's Ratio Auxetic Honeycombs Core.

The combination of auxetic honeycomb and CNT reinforcement composite is expected to further improve the impact protection performance of sandwich structures. This paper studies the low-velocity impact response of sandwich plates with functionally graded carbon nanotubes reinforced composite (FG-CNTRC) face sheets and negative Poisson's ratio (NPR) auxetic honeycomb core. The material properties of FG-CNTRC were obtained by the rule of mixture theory. The auxetic honeycomb core is made of Ti-6Al-4V. The governing equations are derived based on the first-order shear deformation theory and Hamilton's principle. The nonlinear Hertz contact law is used to calculate the impact parameters. The Ritz method with Newmark's time integration schemes is used to solve the response of the sandwich plates. The (20/-20/20)s, (45/-45/45)s and (70/-70/70)s stacking sequences of FG-CNTRC are considered. The effects of the gradient forms of FG-CNTRC surfaces, volume fractions of CNTs, impact velocities, temperatures, ratio of plate length, width and thickness of surface layers on the value of the plate center displacement, the recovery time of deformation, contact force and contact time of low-velocity impact were analyzed in detail.

Comparative Study of Machine Learning Approaches for Predicting Creep Behavior of Polyurethane Elastomer.

The long-term mechanical properties of viscoelastic polymers are among their most important aspects. In the present research, a machine learning approach was proposed for creep properties' prediction of polyurethane elastomer considering the effect of creep time, creep temperature, creep stress and the hardness of the material. The approaches are based on multilayer perceptron network, random forest and support vector machine regression, respectively. While the genetic algorithm and k-fold cross-validation were used to tune the hyper-parameters. The results showed that the three models all proposed excellent fitting ability for the training set. Moreover, the three models had different prediction capabilities for the testing set by focusing on various changing factors. The correlation coefficient values between the predicted and experimental strains were larger than 0.913 (mostly larger than 0.998) on the testing set when choosing the reasonable model.