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Additive manufacturing (AM) enables the fabrication of complex, highly customized geometries. However, the design and fabrication of structures with advanced functionalities, such as multistability and fail-safe mechanism, remain challenging due to the significant time and costs required for high-fidelity simulations and iterative prototyping. In this study, we investigate the application of Bayesian Optimization (BO), an advanced machine learning framework, to accelerate the discovery of optimal AM compatible designs with such advanced properties. BO uses a probabilistic surrogate to strategically balances the exploration of design space with few test designs and the exploitation of design space near current best performing designs, thereby reducing the number of design simulations needed. While existing studies have demonstrated the potential of BO in AM, most have focused on static or simple designs. Here, we target multistable structures that can reconfigure among multiple stable states in response to external conditions. Since mechanical performance (e.g., strength) is configuration-dependent, our goal is to identify high performing designs while ensuring that strength in all stable configurations exceeds a prescribed threshold for structural robustness. 

Shape memory polymer (SMP) systems exhibiting semicrystalline- elastomer blends, such as thermoplastic polyurethane and polylactic acid have been well studied, but their use in biomedical shape memory applications has been limited by their high activation temperature. SMPs are capable of deformation and recovery through the activation of an external stimuli, such as temperature. Critical criteria for SMPs used in biomedical applications is achieving a stimulus temperature close to 37 °C while still experiencing sufficient shape recovery. A polymer’s glass transition temperature has been well defined as the SMP system’s activation temperature and therefore should be decreased to achieve a decreased activation temperature. In this work, a well-known, biocompatible plasticizer, polyethylene oxide (PEO), was added to thermoplastic polyurethane (TPU)—polylactic acid (PLA) SMP blends to observe the plasticizing effect on the structural, thermal, mechanical, and shape memory properties of the polymer blends. Additionally, the geometry of the fabricated SMP samples was tailored to further enhance the shape memory effect through a bowtie honeycomb structure. Our results suggest that the addition of PEO into theSMPsystem may be an effective method for decreasing the polymer’s glass transition temperature through the alteration of the polymer chain structure. With the addition of 30% PEO, the glass transition temperature of the TPU/PLA blend was successfully decreased from 62.4 °Cto 34.6 °Cwhile achieving 86.5% shape recovery when activated at 37 °C, which is only a5%decrease in shape recovery when activated at 50 °C. These results suggest that the addition of a biocompatible plasticizer may overcome the limitation of employing temperature activated SMP systems in biomedical applications, and enhances the potential of these materials for reconfigurable structures, energy dissipation systems, and structural health monitoring (SHM) in civil engineering applications.