3D metal lattice structure manufacturing with continuous rods

In-situ particle analysis with heterogeneous background: a machine learning approach

We propose a novel framework that combines state-of-the-art deep learning approaches with pre- and post-processing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size analyzers and those based on dilution, image processing, or deep learning, typically excel with homogeneous backgrounds. Yet, they often fall short in accurately detecting particles against the intricate and varied backgrounds characteristic of heterogeneous particle-substrate (HPS) interfaces in manufacturing. To address this, we've developed a flexible framework designed to detect particles in diverse environments and input types. Our modular framework hinges on model selection and AI-guided particle detection as its core, with preprocessing and postprocessing as integral components, creating a four-step process. This system is versatile, allowing for various preprocessing, AI model selections, and post-processing strategies. We demonstrate this with an entrainment-based particle delivery method, transferring various particles onto substrates that mimic the HPS interface. By altering particle and substrate properties (e.g., material type, size, roughness, shape) and process parameters (e.g., capillary number) during particle entrainment, we capture images under different ambient lighting conditions, introducing a range of HPS background complexities. In the preprocessing phase, we apply image enhancement and sharpening techniques to improve detection accuracy. Specifically, image enhancement adjusts the dynamic range and histogram, while sharpening increases contrast by combining the high pass filter output with the base image. We introduce an image classifier model (based on the type of heterogeneity), employing Transfer Learning with MobileNet as a Model Selector, to identify the most appropriate AI model (i.e., YOLO model) for analyzing each specific image, thereby enhancing detection accuracy across particle-substrate variations. Following image classification based on heterogeneity, the relevant YOLO model is employed for particle identification, with a distinct YOLO model generated for each heterogeneity type, improving overall classification performance. In the post-processing phase, domain knowledge is used to minimize false positives. Our analysis indicates that the AI-guided framework maintains consistent precision and recall across various HPS conditions, with the harmonic mean of these metrics comparable to those of individual AI model outcomes. This tool shows potential for advancing in-situ process monitoring across multiple manufacturing operations, including high-density powder-based 3D printing, powder metallurgy, extreme environment coatings, particle categorization, and semiconductor manufacturing.

Mechanical Performance of Three-Dimensional Printed Lattice Structures: Assembled Versus Direct Print

Lattice structures are composed of a collection of struts with different orientations. During slicing, the inclined struts generate multiple disjoint contours along the build direction in additive manufacturing (AM). These contours are substantially smaller in size due to the narrow cross-section of the individual lattice struts, and they can lead to contour plurality in AM processes. Contour plurality reduces the amount of continuous contact region between two successive layers, thus resulting in poor interlayer adhesion, structural integrity, and mechanical properties of the printed lattice structure. A new interlocking and assemble-based lattice structure building approach is investigated by increasing continuity in layers and avoiding support structure to minimize contour plurality. Two lattice configurations in the form of cubic and octet lattice structures are examined. The compressive performance of the designed lattice structures is compared with the traditional single-build direct three-dimensional printed lattice structures. The mechanical performance (e.g., peak stress, specific energy absorption) of the assembled structures is found to be generally better than their direct print counterparts. The empirical constants of Ashby-Gibson power law are found to be larger than their suggested values in both direct print and assembly techniques. However, their values are more compliant for octet assembled structures, which are less susceptible to manufacturing imperfections.

Topological transformability and reprogrammability of multistable mechanical metamaterials

Concepts from quantum topological states of matter have been extensively utilized in the past decade to create mechanical metamaterials with topologically protected features, such as one-way edge states and topologically polarized elasticity. Maxwell lattices represent a class of topological mechanical metamaterials that exhibit distinct robust mechanical properties at edges/interfaces when they are topologically polarized. Realizing topological phase transitions in these materials would enable on-and-off switching of these edge states, opening opportunities to program mechanical response and wave propagation. However, such transitions are extremely challenging to experimentally control in Maxwell topological metamaterials due to mechanical and geometric constraints. Here we create a Maxwell lattice with bistable units to implement synchronized transitions between topological states and demonstrate dramatically different stiffnesses as the lattice transforms between topological phases both theoretically and experimentally. By combining multistability with topological phase transitions, this metamaterial not only exhibits topologically protected mechanical properties that swiftly and reversibly change, but also offers a rich design space for innovating mechanical computing architectures and reprogrammable neuromorphic metamaterials. Moreover, we design and fabricate a topological Maxwell lattice using multimaterial 3D printing and demonstrate the potential for miniaturization via additive manufacturing. These design principles are applicable to transformable topological metamaterials for a variety of tasks such as switchable energy absorption, impact mitigation, wave tailoring, neuromorphic metamaterials, and controlled morphing systems.

Micro-particle entrainment from density mismatched liquid carrier system

Micro-scale inorganic particles (d > 1 µm) have reduced surface area and higher density, making them negatively buoyant in most dip-coating mixtures. Their controlled delivery in hard-to-reach places through entrainment is possible but challenging due to the density mismatch between them and the liquid matrix called liquid carrier system (LCS). In this work, the particle transfer mechanism from the complex density mismatching mixture was investigated. The LCS solution was prepared and optimized using a polymer binder and an evaporating solvent. The inorganic particles were dispersed in the LCS by stirring at the just suspending speed to maintain the pseudo suspension characteristics for the heterogeneous mixture. The effect of solid loading and the binder volume fraction on solid transfer has been reported at room temperature. Two coating regimes are observed (i) heterogeneous coating where particle clusters are formed at a low capillary number and (ii) effective viscous regime, where full coverage can be observed on the substrate. 'Zero' particle entrainment was not observed even at a low capillary number of the mixture, which can be attributed to the presence of the binder and hydrodynamic flow of the particles due to the stirring of the mixture. The critical film thickness for particle entrainment is [Formula: see text] for 6.5% binder and [Formula: see text] for 10.5% binder, which are smaller than previously reported in literature. Furthermore, the transferred particle matrices closely follow the analytical expression (modified LLD) of density matching suspension which demonstrate that the density mismatch effect can be neutralized with the stirring energy. The findings of this research will help to understand this high-volume solid transfer technique and develop novel manufacturing processes.

A Novel Rapid Manufacturing Process for Metal Lattice Structure

A novel lattice structure manufacturing process is proposed in this article, which has the potential to overcome the manufacturing shortcomings of small-scale metal lattice structure. The proposed hierarchical process has four segments: Design, Bending, Dip, and Join (DBDJ). The proposed research use one-dimensional metallic wires/rods instead of powder, two-dimensional sheet, or liquid metal, which is highly transformative than the status quo. The topology-based design technique is focused to construct the lattice structure using a continuous thin rod. The layers are stacked in an additive manner to construct the three-dimensional lattice structure. The dip-coating meditate material transfer facilitates the node joining using transient liquid phase diffusion bonding, and hence, the manufacturing of the complex lattice structure is performed. The research framework provides a unique and holistic approach from design to manufacturing for realizing small-scale metallic lattice structure. A range of multiscale lattice structure is manufactured with the proposed DBDJ process. Very low relative density (∼3.8%) unit cell is achieved, and compressive tests demonstrate no failure at the joining node, which is reported in this article.