Computational Modeling and Optimization of Shape Memory Polymer-Based Energy Absorbers

Shape memory polymers (SMPs) provide tunable thermomechanical properties and enable the design of recoverable crash structures for automotive applications. This paper introduces a computational framework for the design and optimization of SMP-based crash absorbers with periodic auxetic microstructures. First, a finite element (FE) model is developed and validated against experimental data regarding crushing and recovery behavior. A parametric study is then performed by varying key microstructural features, including wall thickness, cell size, and cell shape. Structural performance is evaluated in terms of specific energy absorption (SEA), peak force, and recoverability. To efficiently explore the high-dimensional design space, surrogate models based on machine learning are constructed, and multi-objective optimization is carried out to identify Pareto-optimal designs that balance competing objectives. The parametric study indicated that geometric parameters strongly influenced energy absorption and recoverability. Increasing the wall thickness enhanced both stiffness and peak force but reduced recoverability due to higher residual deformation. Larger re-entrant angles (70°–75°) improved auxeticity and distributed stress more uniformly. In addition, the structural configuration representing a balanced performance with moderate peak load and substantial energy recovery has been identified.