Search

This paper introduces an approach for the geometric modeling of advanced cellular structures, characterized by their stochastic, conformal nature, and the ability to transition between multiple topologies. Utilizing a function representation-enhanced topological skeletal graph methodology, this approach enhances computer-aided design by encapsulating complex geometries and parameters within a versatile function representation framework. The novelty of this work lies in its operationalization through LatticeQuery, an open-source software, which demonstrates versatility in modeling stochastic cellular structures, ensures smooth topology transitions, and supports the design of conformal cellular structures. This exploration not only validates the potential of the introduced methodology but also opens new avenues in the design of cellular structures with intricate geometries and variable properties, catering to a wide range of industrial applications. [Display omitted] • A novel cellular modeling method blending function representation with skeletal graphs for enhanced design flexibility. • Seamless topology transitions in cellular structures for complex design adaptability. • Utilizes open-source tools for advanced cellular structure generation, setting a foundation for future innovation. [ABSTRACT FROM AUTHOR]

Highlights • A joint stiffening element is proposed to simulate lattice structures fabricated by additive manufacturing. • The influence of the joint on the stiffness of the lattice structure can be considered in the proposed model. • A parametric study is conducted to quantify the influence of the joint on a certain lattice structure and to find the optimal parameters of the joint stiffening element. • Three-point bend testing is conducted, and it shows that the accuracy is significantly improved by the proposed model for bending dominant lattice structures. • The computational cost of the proposed model is much lower than the tetrahedral element model. Abstract In this paper, a concept called joint stiffening element is proposed to represent the influence of the joint on the stiffness of lattice structures fabricated by additive manufacturing. Four parameters are defined in the proposed element to quantify the increment of the stiffness caused by the lattice joint. Then, three-point-bend testing is used to validate the proposed model. The lattice structures with Cubic-center and Vintiles topologies are designed and fabricated for the experiment. As-fabricated material properties are obtained by tensile specimens to eliminate the influence caused by the manufacturing process and to isolate the joint stiffening effect. The result shows that models with the solid element and the proposed element are both accurate, but the proposed model has a significantly less computational cost than the solid element model. Furthermore, compared to the beam element model, the proposed model can more accurately estimate the stiffness of lattice structures, especially for the Vintiles topology. It is also found that the joint stiffening effect is more significant on the bending dominant topology than the stretching dominant topology. Graphical abstract Image, graphical abstract [ABSTRACT FROM AUTHOR]

The recycling of Carbon Fibers Reinforced Plastics (CFRP) wastes is becoming increasingly important in the aerospace industry. For most of the technologies, the recycled CF (rCF) are discontinuous, misaligned and unsized. Compared to thermal treatments, the orientation, the length and the brittleness of the rCF are better preserved with the SuperCritical Water Solvolysis (SCWS). The effect of the sizing removal on the recycled CFRP behavior is studied by conducting static characterizations. Realigned virgin CF were used to manufacture sample plates which underwent short beam shear tests. An environmental assessment was carried out on the CFRP end-of-life with a focus on SCWS. [ABSTRACT FROM AUTHOR]

Abstract: With the manufacturing industry shifting from seller markets to buyer markets, cognitive capabilities have become enablers for high flexibility and changeability that facilitate efficient production for the fast changing markets. Process planning in such a manufacturing system needs to be adaptive to machine tools and manufacturing environments. Real-time acquisition of production and process data and feedback to process control is essential. However, the current information flow between manufacturing processes is segmented due to the lack of a consolidated data model to represent information. This paper presents a consolidated data model and system structure for an automatic and integrated process planning for machining, inspection, and feedback. The data model is based on the newly developed ISO standard STEP/STEP-NC, which provides semantic level information for the manufacturing system. The system structure and algorithms developed for the system are described first. A software prototype was developed and used to test the proposed data model and the integrated process planning and feedback system. The system provides important cognitive capability, such as automatic on-line monitoring, automated reasoning and adjustment to unforeseen changes during machining processes, for a manufacturing system. This facilitates the realization of cognitive manufacturing in the near future. [Copyright &y& Elsevier]

Additive Manufacturing (AM) also known as 3D printing technology has drawn increasing attention from the industrial world. It provides the capability of freeform fabrication to achieve high complexity as well as significant reduction of a supply chain which further enhances the profit margin for manufacturers. However, while claiming on the advantage on the environmental aspect, there is very limited research on the environmental impact of AM technologies. Most of existing environmental assessment models for AM processes are developed based on a general Life Cycle Assessment (LCA) framework. Due to the limited scope and boundary of these models, design results are usually taken as the input. These existing models may work well for traditional manufacturing processes. However, when it comes to AM process, these methods are no longer valid, since the design freedoms which may have a great impact on sustainability are always neglected. To deal with this issue, a general framework which can integrate a design stage in LCA for minimizing the product environmental impact of AM process in particular for binder-jetting process is proposed. The detailed description of each major stage of the proposed framework is presented. This framework has been applied to evaluate the environmental impact of fabricating an engine bracket by binder-jetting process. In this case study, a comparison of environmental impact between CNC and binder-jetting fabrication process is made. The result shows that binder-jetting consumes significantly less energy and produce less CO 2 to produce a topologically optimized part than CNC milling for the same product. This case study demonstrates a key role of design optimization in the proposed framework. This proposed framework can be further modified for other typical AM process in the future. [ABSTRACT FROM AUTHOR]

The progress of machine learning (ML) in the past years has opened up new opportunities to the field of gas turbine (GT) modelling. However, successful implementation of ML algorithms remains challenging, particularly for complex problems such as multi-mode faults. An important tool for enabling applications are the feature selection and feature learning (FSFL) techniques. In particular, FL techniques have recently facilitated and improved the applicability of ML to GT modelling. This review paper conducts a review on 46 studies that utilised FSFL for GT modelling with ML. The purpose of this review is to investigate how FSFL techniques can help address GT modelling challenges and when researchers should deploy them. Therefore, the theories behind the techniques are illustrated in depth along with practical application examples from the analysed literature. The advantages and limitations of FSFL are discussed, the computational costs of different techniques are compared, and trends in the field are highlighted. Consequently, a novel categorisation framework for FSFL techniques and recommendations regarding when and how to implement them are provided. A new knowledge accumulation, extraction, and transfer concept is proposed to address GT modelling challenges. [ABSTRACT FROM AUTHOR]

Unlike traditional manufacturing methods, additive manufacturing can produce parts with complex geometric structures without significant increases in fabrication time and cost. One application of additive manufacturing technologies is the fabrication of customized lattice-skin structures which can enhance performance of products while minimizing material or weight. In this paper, a novel design method for the creation of periodic lattice structures is proposed. In this method, Functional Volumes (FVs) and Functional Surfaces (FSs) are first determined based on an analysis of the functional requirements. FVs can be further decomposed into several sub-FVs. These sub-FVs can be divided into two types: FV with solid and FV with lattice. The initial design parameters of the lattice are selected based on the proposed guidelines. Based on these parameters, a kernel based lattice frame generation algorithm is used to generate lattice wireframes within the given FVs. At last, traditional bidirectional evolutionary structural optimization is modified to optimize distribution of lattice struts’ thickness. The design method proposed in this paper is validated through a case study, and provides an important foundation for the wide adoption of additive manufacturing technologies in the industry. [ABSTRACT FROM AUTHOR]

Data-driven applications are penetrating every aspect of Additive Manufacturing (AM) to enable efficient resolution of key existing challenges. One such application is semantic segmentation which automatically quantifies post-process structure data from graphic or 3D computer representations. In industrial settings, these models can expedite the development of new materials or processes. However, Additively Manufactured (AMed) materials offer unique challenges, which require new models to be developed and trained. These challenges include out-of-balance classes (e.g., defects), massive labeling efforts, and significant data preparation costs. Recent applications of semantic segmentation in AM have shown the potential of ensemble-based approaches at the expense of increased parameters and computational burden while ignoring the minority classes. In this work, we propose a reproducible method and develop an associated tool to rapidly segment and subsequently quantify industrial AMed metallographic images. First, two new datasets representing AMed materials are generated from extensive experimentation. Subsequently, state-of-the-art models from convolutional and self-attention categories are evaluated in their ability to segment the datasets of interest. Finally, a modular software tool is developed for industrial applications. The developed Microstructure Segmentation, Quantification, and Fusion (+) tool or MicroSegQ+ enables a weighted pairing of predictions from different semantic segmentation models. The pairing strategy helps to exploit the complimentary performance of convolutional and transformer models on multi-class metallographic images from a Metal Matrix Composite (MMC) material system. In addition to developing models on this MMC dataset, the proposed strategy is evaluated on an open-source metal AM dataset. We also evaluate the generalizability of the best-performing model on a second MMC dataset from a different industrial setting that shows an overall accuracy of 93 % without fine-tuning. The proposed tool enables rapid quantification under 1 min, unlike the existing semi-automatic approach that takes 1–2 h of segmentation effort on single bead cross-sections. The tool is wrapped into a single executable file for industrial deployment. The work also contributes to two annotated datasets from a direct energy deposition (DED) AM process. The datasets can be used to develop and validate new semantic segmentation models of AMed microstructures, whereas the functionality of the tool can be expanded upstream to include data and modeling steps, enabling a no-code pipeline for the industry. [ABSTRACT FROM AUTHOR]

Due to the remarkable ability of Deep learning (DL) to abstract hidden information, it has been proven to be a powerful tool in many tasks related to the design of metamaterials. DL-aided design techniques can be generally categorized into two types, including forward designs, which are training surrogate models to accelerate the simulation process, and inverse design, which uses inverse modeling techniques to generate the design that satisfies the corresponding requirement. Although generative models have a unique capability to generate multiple designs instantly with random information, they often underperform in accuracy compared to designs based on optimization techniques. In this paper, a hybrid design framework combining the advantages of both DL forward design and the inverse design based on the mixture density network (MDN) is proposed. Then the proposed framework is implemented for the inverse design of S-shaped perforated auxetic metamaterial. The hybrid design framework inherited the one-to-many mapping capability of MDN and has great capability of generating designs with designated mechanical properties at less than 10% relative errors, in most design scenarios (over 95% in the test set), at one to two orders of magnitude less computational cost compared to optimization-based forward design. • A hybrid method with MDN and DNN generates designs at low cost but good accuracy. • A case study of auxetic metamaterial shows the effectiveness of the hybrid method. • The comparison with current DL design models shows the advantage of our model. [ABSTRACT FROM AUTHOR]

Hydroxyapatite (HA, Ca 5 (PO 4 ) 3 OH) has been extensively used for bone implantation due to its similarity to the mineral component of bone, which makes it strongly osteoconductive. However, HA has low resorbability, and it is difficult to replace by a newly regenerated bone. Si doping can enhance the resorbability of HA by modifying its crystal structure. Here, we developed a simple thermal technique for preparing Si-doped HA from silica (SiO 2 ) and HA precursors, both of which are inexpensive and commercially available. This method included the physical binding of SiO 2 and HA particles, followed by pressing and sintering the mixture at an elevated temperature, which enhanced the atomic diffusion of Si into HA unit cells. We also evaluated the simulated body fluid (SBF) activity of the Si-doped HA prepared by this technique and showed that it significantly had higher resorbability and mineralizing potential compared to the pure HA. Our experimental design including, the individual precipitation and resorption assays enabled us to explain the mechanism behind the improved activity of Si-doped HA in SBF. This was attributed to the formation of new phases, such as β-tricalcium phosphate (β-TCP) and calcium silicate (Ca 2 SiO 4 ) with higher solubility than HA on the SiO 2 -contating HA during the sintering stage. This can provide some guidelines for designing new calcium phosphate-based materials for hard tissue engineering applications. [ABSTRACT FROM AUTHOR]

Auxetic metamaterials have been applied in many domains due to their unique auxetic behavior, tunable local kinematics, and morphological intelligence. However, the classic auxetic designs normally cannot be directly applied due to their defects. How to modify the existing classic design to achieve diverse but also valid variant designs remains a challenging problem. One common approach is to use predefined parameters and fine-tune these parameters. Yet, this approach limits the design space and highly relies on expert knowledge. In this paper, we proposed a variant design generation method based on a path-finding algorithm to achieve pixel-level design freedom. Meanwhile, to support the evaluation of designs in such a large design space, we developed a multi-scale geometry-informed Graph U-net. More specifically, we proposed MeshPool—a predefined clustering-based pooling technique, and distortion loss—a geometry-informed loss function inspired by finite element analysis (FEA). A variant design family for re-entrant structures is generated as a case study to validate the variant design generation method. And the Graph U-net is developed to predict complex nonlinear deformation with simple linear FEA results. The results from the case study demonstrated that the proposed variant design generation method covered the properties space of re-entrant structures and the developed Graph U-net achieved accurate prediction with a nodal displacement mean absolute error (MAE) of around 0.0169 mm under an average nodal displacement of 0.5058 mm. More importantly, we demonstrated the graph neural network's unique capability of learning gradient information compared with convolutional neural network in a benchmark on strain and stress. • Path-finding algorithm generates pixel-level auxetic designs for three classic types. • Algorithm enhances diversity, reduces duplicates, improves training data fairness. • Multi-scale GNN predicts auxetic deformation using geometry-informed pooling. • Geometry-informed loss term enforces physical constraints efficiently. [ABSTRACT FROM AUTHOR]

[Display omitted] • A literature review on the heat and flow properties of lattice structures is performed, and design parameters are identified. • Detailed geometric modeling and clear the knowledge gaps in the flow and heat properties of various lattices. • The results are compared with the findings obtained from existing literature and documented in detailed charts. Lattice structures are known for their capability to be tailored for achieving specific properties such as high porosity and strength, impact energy absorption and lightweight. While the mechanical properties of the strut-based and surface-based lattice structures have already been investigated in the literature, a systematic investigation of their flow and thermal properties has yet to be established. This work investigates the friction factor and convective heat transfer coefficient across strut- and surface-based lattice structures. The modeled structures are simulated in the convective heat transfer environment to gather the heat transfer coefficient and the friction factor outputs. The simulation results are documented in this work and used to generate a flow and thermal property chart to support the design process by providing a guideline for selecting lattice topologies. [ABSTRACT FROM AUTHOR]

The existing mechanical metamaterials are often designed with periodic inter-connected structs with simple cylindrical or uniform hierarchical geometries, which relies on their parent materials to either have a good mechanical performance with low recoverability, or significantly sacrifices their mechanical performances to be highly recoverable. Biological fibrous structures are often evolved with a composition of different fibrous morphologies to possess a desired balance of mechanical performances and recovery. In this study, we developed digital design algorithms to generate the next-generation metamaterials with composite bio-inspired twisting fibrotic structs that are rubber-like recoverable without significant scarification of their mechanical performances. A machine learning predictive model is trained based on experimental data to reveal the resulted specific energy absorption (SEA) and SEA recoveries for such metamaterials with complicated fiber-composition mechanisms. To further understand the fundamental structural recovery mechanisms of the natural fibers, we derived the elastoplastic theories of the twisting fibrotic structs, and revealed that such structs possesses a rubber-like fracture strain with significantly improved specific energy absorption. Our studies combined the structural recovery mechanisms of the composite natural fibrous structures and mechanical metamaterials, liberates the design potential of materials with engineerable optimal balances of their mechanical performances and recoverability. [Display omitted] • Designed the metamaterials with natural-fiber-inspired (NFI) structs for simultaneous high specific energy absorption (SEA) and SEA recoveries. • Designed, and successfully 3D printed metamaterials with composite NFI morphologies with digital design algorithms. • Trained machine learning aided prediction model of the SEA and SEA recoveries for NFI metamaterials with variations of fibrous morphologies. • Developed NFI metamaterials with programmable rubber-like recoverability without reducing their SEAs through a data-driven approach. [ABSTRACT FROM AUTHOR]