Search for: All records

Abstract Inspired by natural designs, microstructures exhibit remarkable properties, which drive interest in creating metamaterials with extraordinary traits. However, imperfections within microstructures and poor connectivity at the microscale level can significantly impact their performance and reliability. Achieving proper connectivity between microstructural elements and detecting structural imperfections within the microstructures pose challenges in multiscale design optimization. While using a connectivity index (CI) to quantify the topological connectivity between microstructures has been explored previously, prior approaches have limitations in identifying microstructures with complex curved geometries between adjacent units. To alleviate this issue, we present a novel CI in this study. The proposed CI goes beyond conventional methods by focusing on surface interfaces and internal microstructural irregularities. Through numerical investigations, we successfully connected distinct types of microstructures well by integrating the introduced CI with the functional gradation scheme. We also demonstrate that the presented CI can serve as a metric to identify sharp changes or imperfections within microstructures. We evaluate the performance of the introduced index against other connectivity indices using diverse microstructural examples. Experimental findings provide valuable insights into the fundamental aspects of imperfection detection and rectification in microstructures within the multiscale design, paving the way for developing more robust and reliable materials in engineering applications. 

Abstract The surge in machine learning research and recent advancements in 3D printing technologies have significantly enriched materials science and engineering, particularly in the domain of mechanical metamaterials, which commonly consist of periodic truss materials. Despite the extensive exploration of their tailorable properties, truss-based metamaterial design has predominantly adhered to cubic and orthotropic unit cells, a limitation arising from the conventional design method, where the type of symmetry related to the designed truss-based material is determined after the design process is done. To overcome this issue, this work introduces a groundbreaking 3D truss material designing framework that departs from this constraint by employing six distinctive material symmetries (cubic, hexagonal, tetragonal, orthotropic, trigonal, and monoclinic) within the design process. This innovative approach represents a versatile paradigm shift compared to previous design approaches. Furthermore, we are able to integrate anisotropy into the design framework, thus enhancing the property space exploration capability of the proposed design framework. Probing the property space of unit cells using our design framework demonstrates its capacity to achieve a diverse range of mechanical properties. The analysis of the generated samples shows that they can surpass the most extensive datasets available in the literature in regions where directional elastic properties are not linked by structural symmetry. The proposed method facilitates the generation of a truss dataset, which can be represented in a trainable format suitable for machine learning and data-driven approaches. This advancement paves the way for the development of robust inverse design tools for truss materials, marking a significant contribution to the mechanical metamaterial community. 

Abstract Inspired by natural designs, microstructures exhibit remarkable properties, which drive interest in creating metamaterials with extraordinary traits. However, imperfections within microstructures and poor connectivity at the microscale level can significantly impact their performance and reliability. Achieving proper connectivity between microstructural elements and detecting structural imperfections within the microstructures pose challenges in multiscale design optimization. While using a connectivity index (CI) to quantify the topological connectivity between microstructures has been explored previously, prior approaches have limitations in identifying microstructures with complex curved geometries between adjacent units. To alleviate this issue, we present a novel CI in this study. The proposed CI goes beyond conventional methods by focusing on surface interfaces and internal microstructural irregularities. Through numerical investigations, we successfully connected distinct types of microstructures well by integrating the introduced CI with the functional-gradation scheme. We also demonstrate that the presented CI can serve as a metric to identify sharp changes or imperfections within microstructures. We evaluate the performance of the introduced index against other connectivity indices using diverse microstructural examples. Experimental findings provide valuable insights into the fundamental aspects of imperfection detection and rectification in microstructures within the multiscale design, paving the way for developing more robust and reliable materials in engineering applications. 

Abstract The surge in machine learning research and recent advancements in 3D printing technologies have significantly enriched materials science and engineering, particularly in the domain of mechanical metamaterials, which commonly consist of periodic truss materials. Despite the extensive exploration of their tailorable properties, truss-based metamaterial design has predominantly adhered to cubic and orthotropic unit-cells, a limitation arising from the conventional design method, where the type of symmetry related to the designed truss-based material is determined after the design process is done. To overcome this issue, this work introduces a groundbreaking 3D truss material designing framework that departs from this constraint by employing six distinctive material symmetries (cubic, hexagonal, tetragonal, orthotropic, trigonal, and monoclinic) within the design process. This innovative approach represents a versatile paradigm shift compared to previous design approaches. Furthermore, we are able to integrate anisotropy into the design framework, thus enhancing the property space exploration capability of the proposed design framework. Probing materials property space using our design framework demonstrates its capacity to achieve a diverse range of mechanical properties, surpassing even the most extensive datasets available in the literature. The proposed method facilitates the generation of a comprehensive truss dataset, which can be represented in a trainable continuous format suitable for machine learning and data-driven approaches. This advancement paves the way for the development of robust inverse design tools for truss materials, marking a significant contribution to the mechanical metamaterial community. 

Abstract Inkjet 3D printing has broad applications in areas such as health and energy due to its capability to precisely deposit micro-droplets of multi-functional materials. However, the droplet of the inkjet printing has different jetting behaviors including drop initiation, thinning, necking, pinching and flying, and they are vulnerable to disturbance from vibration, material inhomogeneity, etc. Such issues make it challenging to yield a consistent printing process and a defect-free final product with desired properties. Therefore, timely recognition of the droplet behavior is critical for inkjet printing quality assessment. In-situ video monitoring of the printing process paves a way for such recognition. In this paper, a novel feature identification framework is presented to recognize the spatiotemporal feature of in-situ monitoring videos for inkjet printing. Specifically, a spatiotemporal fusion network is used for droplet printing behavior classification. The categories are based on inkjet printability, which is related to both the static features (ligament, satellite, and meniscus) and dynamic features (ligament thinning, droplet pinch off, meniscus oscillation). For the recorded droplet jetting video data, two streams of networks, the frames sampled from video in spatial domain (associated with static features) and the optical flow in temporal domain (associated with dynamic features), are fused in different ways to recognize the droplet evolving behavior. Experiments results show that the proposed fusion network can recognize the droplet jetting behavior in the complex printing process and identify its printability with learned knowledge, which can ultimately enable the real-time inkjet printing quality control and further provide guidance to design optimal parameter settings for the inkjet printing process. 

Many industries, such as human-centric product manufacturing, are calling for mass customization with personalized products. One key enabler of mass customization is 3D printing, which makes flexible design and manufacturing possible. However, the personalized designs bring challenges for the shape matching and analysis, owing to the high complexity and shape variations. Traditional shape matching methods are limited to spatial alignment and finding a transformation matrix for two shapes, which cannot determine a vertex-to-vertex or feature-to-feature correlation between the two shapes. Hence, such a method cannot measure the deformation of the shape and interested features directly. To measure the deformations widely seen in the mass customization paradigm and address the issues of alignment methods in shape matching, we identify the geometry matching of deformed shapes as a correspondence problem. The problem is challenging due to the huge solution space and nonlinear complexity, which is difficult for conventional optimization methods to solve. According to the observation that the well-established massive databases provide the correspondence results of the treated teeth models, a learning-based method is proposed for the shape correspondence problem. Specifically, a state-of-the-art geometric deep learning method is used to learn the correspondence of a set of collected deformed shapes. Through learning the deformations of the models, the underlying variations of the shapes are extracted and used for finding the vertex-to-vertex mapping among these shapes. We demonstrate the application of the proposed approach in the orthodontics industry, and the experimental results show that the proposed method can predict correspondence fast and accurate, also robust to extreme cases. Furthermore, the proposed method is favorably suitable for deformed shape analysis in mass customization enabled by 3D printing. 

Efficient disassembly operation is considered a promising approach toward waste reduction and End-of-Use (EOU) product recovery. However, many kinds of uncertainty exist during the product lifecycle which make disassembly decision a complicated process. The optimum disassembly sequence may vary at different milestones depending on the purpose of disassembly (repair, maintenance, reuse and recovery), product quality conditions and external factors such as consumer preference, and the market value of EOU components. A disassembly sequence which is optimum for one purpose may not be optimum in future life cycles or other purposes. Therefore, there is a need for incorporating the requirements of the entire product life-cycle when obtaining the optimum disassembly sequence. This paper applies a fuzzy method to quantify the probability that each feasible disassembly transition will be needed during the entire product lifecycle. Further, the probability values have been used in an optimization model to find the disassembly sequence with maximum likelihood. An example of vacuum cleaner is used to show how the proposed method can be applied to quantify different users’ evaluation on the relative importance of disassembly selection criteria as well as the probability of each disassembly operation.