High capacity, adaptive energy absorption under tensile loading conditions utilizing an axial cutting deformation mode

•Axial cutting studied under tensile loading conditions for AA6061 extrusions.•Extrusions with various temper conditions and wall thickness profiles considered.•High load bearing capacity and excellent repeatability demonstrated experimentally.•Analytical model utilized to design adaptive energy absorbers with unique abilities.•Lagrangian–Eulerian simulations replicated findings with high degree of correlation. Conventional tensile energy absorbers are often limited in their efficacy by erratic and unpredictable force responses. Additionally, the published literature on devices of this nature is sparser in comparison to compressive energy absorbers and hence engineers are further limited by a lack of existing designs. Axial cutting is an energy dissipating technology studied extensively under compressive loading with promising results. A novel apparatus was explored in this study to implement axial cutting under tensile loading; this is an application which sees significantly less attention in the open literature. An analytical modeling approach was utilized as a design tool to assess the specimens in this study and to precisely engineer energy absorbers with adaptive force responses. The tests were conducted quasi-statically utilizing a hydraulically powered testing apparatus with a capacity of 300 kN. AA6061-T6 and T4 extrusions were utilized with wall thicknesses ranging from 0.794 mm to 3.175 mm. Force responses with tensile force efficiencies between 85% and 92% were observed. Energy absorption values ranging from 2.2 kJ to 7.7 kJ and specific energy absorption values between 12 kJ/kg to 16 kJ/kg were measured, greatly exceeding the mechanical capabilities of multiple, established tensile energy dissipating solutions. Highly stable and repeatable deformation was observed between consecutive tests within most specimen categories. Numerical models were created utilizing LS-DYNAⓇ and average validation metrics and cumulative errors of approximately 0.90 and 0.09 were calculated, respectively, indicating excellent predictive capabilities.