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Abstract Biopolymers and bioinspired materials contribute to the construction of intricate hierarchical structures that exhibit advanced properties. The remarkable toughness and damage tolerance of such multilevel materials are conferred through the hierarchical assembly of their multiscale (i.e., atomistic to macroscale) components and architectures. Here, the functionality and mechanisms of biopolymers and bio‐inspired materials at multilength scales are explored and summarized, focusing on biopolymer nanofibril configurations, biocompatible synthetic biopolymers, and bio‐inspired composites. Their modeling methods with theoretical basis at multiple lengths and time scales are reviewed for biopolymer applications. Additionally, the exploration of artificial intelligence‐powered methodologies is emphasized to realize improvements in these biopolymers from functionality, biodegradability, and sustainability to their characterization, fabrication process, and superior designs. Ultimately, a promising future for these versatile materials in the manufacturing of advanced materials across wider applications and greater lifecycle impacts is foreseen. 

Three-dimensional (3D) printing can be beneficial to tissue engineers and the regenerative medicine community because of its potential to rapidly build elaborate 3D structures from cellular and material inks. However, predicting changes to the structure and pattern of printed tissues arising from the mechanical activity of constituent cells is technically and conceptually challenging. This perspective is targeted to scientists and engineers interested in 3D bioprinting, but from the point of view of cells and tissues as mechanically active living materials. The dynamic forces generated by cells present unique challenges compared to conventional manufacturing modalities but also offer profound opportunities through their capacity to self-organize. Consideration of self-organization following 3D printing takes the design and execution of bioprinting into the fourth dimension of cellular activity. We therefore propose a framework for dynamic bioprinting that spatiotemporally guides the underlying biology through reconfigurable material interfaces controlled by 3D printers. 

Tissue engineering is an interdisciplinary field combining biology, chemistry, and engineering to create implantable structures that support healing and regeneration. Autografts, tissues taken from a patient’s own body, are commonly used due to their high compatibility and minimal disease transmission risk. However, autografts are limited by the small amount of tissue that can be harvested. Allografts, or transplants from one person to another, provide a more natural alternative to synthetic or metal implants, yet their use is constrained by limited donor availability, high rejection rates, and significant operating costs. Although synthetic polymer, ceramic, and metallic implants have gained popularity due to their affordability and durability, they often lead to chronic pain, restricted movement, and eventual reimplantation because of issues like surface wear and reduced lubrication. Advances in artificial intelligence (AI), machine learning (ML), and 3D printing are opening new possibilities in tissue engineering. Researchers are now exploring natural polymers as an alternative to synthetic materials by focusing on the structural complexities and sustainability of native tissues. For example, type I collagen (Col), the most abundant protein in human connective tissues, shows promise as a replacement for titanium in bone tissue engineering due to its excellent mechanical properties, biocompatibility, and ability to support bone growth (osteogenesis). When combined with hydroxyapatite (HAp), Col-HAp composites can closely mimic the natural organic-inorganic structure of bone, providing both the chemical and physical properties needed to promote tissue healing and regeneration. However, the extraction and processing of collagen pose challenges, as they can degrade its natural properties and complicate the 3D printing of implants. This perspective examines the processing, characterization, and manufacturability of Col, its composites, and other robust biomaterials for bone tissue engineering, aiming to replicate the mechanical behavior of human limbs under both static and dynamic conditions. It also explores how AI and ML can enhance the precision and reproducibility of Col composite printing and enable generative scaffold design to foster vascularization, cell viability, and tissue growth. Finally, this work underscores the advancements in novel and customized 3D bioprinting systems designed to address patient-specific requirements, promote higher cell proliferation, and fabricate complex scaffold structures with improved structural properties. 

Ensuring high-quality prints in additive manufacturing is a critical challenge due to the variability in materials, process parameters, and equipment. Machine learning models are increasingly being employed for real-time quality monitoring, enabling the detection and classification of defects such as under-extrusion and over-extrusion. Vision Transformers (ViTs), with their global self-attention mechanisms, offer a promising alternative to traditional convolutional neural networks (CNNs). This paper presents a transformer-based approach for print quality recognition in additive manufacturing technologies, with a focus on fused filament fabrication (FFF), leveraging advanced self-supervised representation learning techniques to enhance the robustness and generalizability of ViTs. We show that the ViT model effectively classifies printing quality into different levels of extrusion, achieving exceptional performance across varying dataset scales and noise levels. Training evaluations show a steady decrease in cross-entropy loss, with prediction accuracy, precision, recall, and the harmonic mean of precision and recall (F1) scores reaching close to 1 within 40 epochs, demonstrating excellent performance across all classes. The macro and micro F1 scores further emphasize the ability of ViT to handle both class imbalance and instance-level accuracy effectively. Our results also demonstrate that ViT outperforms CNN in all scenarios, particularly in noisy conditions and with small datasets. Comparative analysis reveals ViT advantages, particularly in leveraging global self-attention and robust feature extraction methods, enhancing its ability to generalize effectively and remain resilient with limited data. These findings underline the potential of the transformer-based approach as a scalable, interpretable, and reliable solution to real-time quality monitoring in FFF, addressing key challenges in additive manufacturing defect detection and ensuring process efficiency.