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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. 

The goal of this work is the flaw-free, industrial-scale production of biological additive manufacturing of tissue constructs (Bio-AM). In pursuit of this goal, the objectives of this work in the context of extrusion-based Bio-AM of bone tissue constructs are twofold: (1) detect flaw formation using data from in-situ infrared thermocouple sensors; and (2) prevent flaw formation through preemptive process control. In realizing the first objective, data signatures acquired from in-situ sensors were analyzed using several machine learning approaches to ascertain critical quality metrics, such as print regime, strand width, strand height, and strand fusion severity. These quality metrics are intended to capture the process state at the basic 1D strand-level to the 2D layer-level. For this purpose, machine learning models were trained to classify and predict flaw formation. These models predicted print quality features with accuracy nearing 90%. In connection with the second objective, the previously trained machine learning models were used to preempt flaw formation by changing the process parameters (print velocity) during deposition—a form of feedforward control. With the feedforward process control, strand width heterogeneity was statistically significantly reduced, reducing the strand width difference between strand halves to less than 50 µm. Using this integrated process monitoring, detection, and control approach, we demonstrate consistent, repeatable production of Bio-AM constructs. 

Biological additive manufacturing (Bio-AM) has emerged as a promising approach for the fabrication of biological scaffolds with nano- to microscale resolutions and biomimetic architectures beneficial to tissue engineering applications. However, Bio-AM processes tend to introduce flaws in the construct during fabrication. These flaws can be traced to material nonhomogeneity, suboptimal processing parameters, changes in the (bio)-printing environment (such as nozzle clogs), and poor construct design, all with significant contributions to the alteration of a scaffold’s mechanical properties. In addition, the biological response of endogenous and exogenous cells interacting with the defective scaffolds could become unpredictable. In his Review, we first described extrusion-based Bio-AM. We highlighted the salient architectural and mechanotransduction parameters affecting the response of cells interfaced with the scaffolds. The process phenomena leading to defect formation and some of the tools for defect detection are reviewed. The limitations of the existing developments and the directions that the field should grow in to overcome said limitations are discussed.