3D Printing and Additive Manufacturing最新文献

Imagine a world in which architecture will be 3D printed from living materials. That buildings will germinate, bloom, wither, produce new kinds of materials, and return back to the soil. This article introduces an innovative approach to sustainable architecture, through the utilization of 3D-printed structures crafted from locally sourced soil and plant seeds. After printing, the seeds germinate over time, forming load-bearing designs with interwoven root systems, which exhibit remarkable strength and resilience, reducing reliance on conventional construction materials. The research evaluates the mechanical properties of 3D-printed living structures through a set of material experiments to find a material combination that will allow maximum growth within 3D-printed architectural scale objects. The successful pilot project demonstrated their strength and capacity to support plant growth. The study also addresses the esthetic, cultural, and social dimensions of this novel fabrication technique, offering personalized, native plant-based patterns, and fostering community engagement. In conclusion, this research underscores the transformative potential of 3D-printed root-built structures as a sustainable architectural solution. By harnessing local soil and plant roots, these living constructions offer an eco-friendly alternative to conventional materials, with diverse environmental and social benefits. This study contributes to the evolving knowledge base of eco-conscious building practices, encouraging further exploration and adoption of nature-based solutions in architecture. With ongoing development, root-built buildings hold the promise of revolutionizing design, construction, and habitation, promoting a harmonious coexistence between humans and the natural environment.

This article discusses the evolving use of bioreactors, beyond traditional life sciences and bioengineering, in fields such as architecture, fashion, and product design. It explores the role of bioreactors in additive fabrication, highlighting their distinct characteristics compared with conventional digital manufacturing. The discussion is centered on the differences in materializing biologically-active (living) versus biologically-passive, or biologically-derived (nonliving) matter in which ingredients require closed-loop fabrication environments that differ from traditional additive manufacturing tools. Two novel biofabrication platforms, Microbial Design Studio and B | reactor are presented as examples with case studies demonstrating their use in various manufacturing workflows with live cells. The article emphasizes the unique capabilities of bioreactors in engaging with living matter and facilitating complex interactions between biological, algorithmic, and mechanical systems in additive manufacturing.

3D-printed earth materials that incorporate natural raw soils have been recently emerging due to their ecological and affordability potential. However, earth materials applications in additive manufacturing have been limited to thick mass assemblies with little to no fiber reinforcement. The addition of natural plant fibers within earth-based mixtures may advantageously increase ductility while allowing for lightweight assembly types, such as thin and perforated elements. This article presents a novel research development on natural, raw, and untreated earth-fiber compositions with maximized wheat straw fiber content for 3D-printed lightweight architectural tiling applications. Initiated with an experimental printability apparatus of a range of mix designs, a printable "light straw clay" mixture is defined through extrudability and buildability tests. Then, combining the digital craft of weaving with natural fibers for earthen lightweight artifacts, a geometric analysis explores potential super lightweight and structurally sound tessellations to allow for minimum material in the production of perforated panels. The third phase of the research included structural bending tests to assess the number of layers required for the final tile production. Finally, the resulting 3D-printed modular components were assembled to create a lightweight installation, hung and exhibited with an interplay of light and shade. By maximizing co-product vegetable fiber content within an earthen and bio-based paste, this research aims to increase the carbon storage capabilities of digital earth construction while enhancing its lightness and tensile possibilities. Learning from vernacular "recipes" of natural earth- and fiber-based construction, the developed paper-thin partition assemblage presented in this article contributes to wider possibilities of natural, nonconventional, and radically low-carbon material systems and geometries in digital fabrication.

Four-dimensional printing (4DP) via fused deposition modeling has been used to create hygromorphic biocomposite actuators through wood polymer composite (WPC) filaments. The shape-change transformation of the 4DP composite mechanism is preprogrammed by controlling the printing process parameters and the design of the print-path pattern. Until now, most 4DP approaches involving Wood Polymer Composite (WPCs) have focused on planar actuators featuring a bilayer structure composed of laminar layers with distinct material properties. These mechanisms show a laminar initial rest state, presenting as flat objects, and can only achieve a complex three-dimensional shape when subjected to the moisture variations stimulus. The presented research highlights the development of a multistage printing method that expands the capabilities of three-axis printers to enable the 4DP of mechanism with complex nonplanar rest-state geometries. The new technical capabilities of this method are demonstrated here through the creation and testing of novel nonlaminar 4DP mechanisms that harness their unique doubly curved rest-state geometry to achieve kinematic amplification. We expect that this approach can greatly improve the range and complexity of 4DP mechanisms that can be developed using the commonly available three-axis printers.

The article asks how additive manufacturing for the circular bioeconomy can create the foundation for rethinking the architectural axioms of permanence and durability, instead moving us toward a new ideal of renewability and repair. It presents a case study into additive manufacturing for repair through the 3D printing of biopolymer composites. This case study connects machine vision-based surveying of damaged panels with repair through conformal 3D printing. This deployment of bio-based materials aims to enable additive manufacturing as a method for disrupting the sharp delineation between fabrication and repair leading to new practices of continual construction. With point of departure in our bespoke systems for 3D printing and unique biopolymer composites, we examine how their particular material characteristics allow for material adhesion and buildup and how novel methods for iterative 3D printing can support design integrated strategies of repair. As part of this process, we include the sociotechnological dimension, as human-in-the-loop decision-making becomes part of the material surveying regimes necessary for damage detection. The article demonstrates processes of repair through three repair actions that address different kinds of damage.

The growing environmental impacts of solid waste accumulation have resulted in an increased demand for biodegradable alternatives to conventional plastics. While several products have begun to gain popularity as biodegradable or compostable plastics, these often still negatively impact terrestrial and aquatic environments, as they frequently require precise conditions in order to fully decompose. Furthermore, standards for measuring biodegradation rates are often complex and poorly representative of real disposal sites, limiting their widespread use and applicability. In this study, we present four simple tests to assess the environmental degradability of materials without specialized equipment and demonstrate them with a series of 3D printable biotic composites composed of pectin, chitosan, and cellulose, abundant and organic biopolymers known to be degradable by common microorganisms. Five different compositions were degraded in live soil, worm burial, high humidity, and aqueous environments, and demonstrated rapid degradation with up to 100% mass loss after 21 days for a pectin-based material buried in worm-laden oil. Degradability was further found to be tunable, with decreasing degradation rate as chitosan content increased. Our results confirm that biotic composites degrade more rapidly than conventional plastics and provide accessible methods that can enable more widespread material testing for the development of sustainable material alternatives, especially to gather basic environmental degradation information representative of typical solid waste discard conditions. We anticipate that these degradation methods and the materials degraded therein will provide further impetus for reducing waste from 3D printing and for considering end of life when designing products.

Additive manufacturing has revolutionized structural optimization by enhancing component strength and reducing material requirements. One approach used to achieve these improvements is the application of multi-lattice structures, where the macroscale performance relies on the detailed design of mesostructural lattice elements. Many current approaches to designing such structures use data-driven design to generate multi-lattice transition regions, making use of machine learning models that are informed solely by the geometry of the mesostructures. However, it remains unclear if the integration of mechanical properties into the dataset used to train such machine learning models would be beneficial beyond using geometric data alone. To address this issue, this work implements and evaluates a hybrid geometry/property variational autoencoder (VAE) for generating multi-lattice transition regions. In our study, we found that hybrid VAEs demonstrate enhanced performance in maintaining stiffness continuity through transition regions, indicating their suitability for design tasks requiring smooth mechanical properties.

The integration of artificial intelligence (AI) into the design and fabrication process has opened up novel pathways for producing custom objects and altered the traditional creative workflow. In this article, we present Depthfusion, a novel text-to-3D model generation system that empowers users to rapidly create detailed 3D models from textual or 2D image inputs, and explore the application of text-to-3D models within different fabrication techniques. Depthfusion leverages current text-to-image AI technologies such as Midjourney, Stable Diffusion, and DALL-E and integrates them with advanced mesh inflation and depth mapping techniques. This approach yields a high degree of artistic control and facilitates the production of high-resolution models that are compatible with various 3D printing methods. Our results include a biomimetic tableware set that merges intricate design with functionality, a large-scale ceramic vase illustrating the potential for additive manufacturing in ceramics, and even a sneaker-shaped bread product achieved by converting AI design into a baked form. These projects showcase the diverse possibilities for AI in the design and crafting of objects across mediums, pushing the boundaries of what is traditionally considered feasible in bespoke manufacturing.

Vat photopolymerization is characterized by its high precision and efficiency, making it a highly promising technique in ceramic additive manufacturing. However, the process faces a significant challenge in the form of recoating defects, necessitating real-time monitoring to maintain process stability. This article presents a defect detection method that leverages multi-image fusion and deep learning for identifying recoating defects in ceramic additive manufacturing. In the image fusion process, multiple single-channel recoating images captured by monitoring camera positioned near the photopolymerization equipment are merged with curing area mask image to create a three-channel color image. The recoating images suffer from perspective distortion due to their side view. To facilitate fusion with the curing area image, image rectification technique is applied to correct the perspective distortion, transforming the side view recoating images into a top-down view. Subsequently, the fused images are processed using a channel-wise YOLO (You Only Look Once, CW-YOLO) method to extract features, enabling the distinction of various types of defects. When compared with other deep learning models, CW-YOLO achieves higher detection accuracy while maintaining a detection rate of 103.58fps, meeting the requirements for real-time detection. Furthermore, the paper introduces the F1 score as a comprehensive evaluation metric, capturing both detection accuracy and recall rate. The results show that the F1 score is enhanced by approximately 10% after image fusion, demonstrating that the proposed method can significantly improve defect detection, particularly in cases involving difficult-to-distinguish defects like material shortages and scratches.