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. A finite element (FE) model is developed and validated against experimental data reported in the literature. The model is used to simulate crushing and recovery behavior under different temperature conditions. A parametric study is 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 under axial crush loading. 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. Sensitivity analysis further reveals the most influential design parameters. Results demonstrate that SMP-based absorbers can deliver energy absorption comparable to or greater than conventional polymer foams while additionally offering partial post-crash recovery, underscoring their potential as sustainable, reusable solutions for vehicle crashworthiness.