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The objective of this paper is to identify suitable lattice structure patterns for the design of porous bone implants manufactured using additive manufacturing.

The study serves to compare and analyse the mechanical behaviours between cubic and octet-truss gradient lattice structures. The method used was uniaxial compression simulations using finite element analysis to identify the translational displacements.

From the simulation results, in comparison to the cubic lattice structure, the octet-truss lattice structure showed a significant difference in mechanical behaviour. In the same design space, the translational displacement for both lattice structures increased as the relative density decreased. Apart from the relative density, the microarchitecture of the lattice structure also influenced the mechanical behaviour of the gradient lattice structure.

Gradient lattice structures are suitable for bone implant applications because of the variation of pore sizes that mimic the natural bone structures. The complex geometry that gradient lattice structures possess can be manufactured using additive manufacturing technology.

The results demonstrated that the cubic gradient lattice structure has the best mechanical behaviour for bone implants with appropriate relative density and pore size.

In recent years, innovative aircraft designs have been investigated by researchers to address the environmental and economic issues for the purpose of green aviation. To keep air transport competitive and safe, it is necessary to maximize design efficiencies of the aircrafts in terms of weight and cost. The purpose of this paper is to focus on the research which has led to the development of a novel lattice fuselage design of a forward-swept wing aircraft in the conceptual phase by topology optimization technique.

In this paper, the fuselage structure is modelled with two different types of elements – 1D beam and 2D shell – for the validation purpose. Then, the finite element analysis coupled with topology optimization is performed to determine the structural layouts indicating the efficient distributed reinforcements. Following that, the optimal fuselage designs are obtained by comparison of the results of 1D and 2D models.

The topological results reveal the need for horizontal stiffeners to be concentrated near the upper and lower extremities of the fuselage cross section and a lattice pattern of criss-cross stiffeners should be well-placed along the sides of the fuselage and near the regions of window locations. The slight influence of windows on the optimal reinforcement layout is observed. To form clear criss-cross stiffeners, modelling the fuselage with 1D beam elements is suggested, whereas the less computational time is required for the optimization of the fuselage modelled using 2D shell elements.

The authors propose a novel lattice fuselage design in use of topology optimization technique as a powerful design tool. Two types of structural elements are examined to obtain the clear reinforcement detailing, which is also in agreement with the design of the DLR (German Aerospace Center) demonstrator. The optimal lattice layout of the stiffeners is distinctive to the conventional semi-monocoque fuselage design and this definitely provides valuable insights into the more efficient utilization of composite materials for novel aircraft designs.

Multimaterial components possess material boundaries that introduce potential points of failure. Graded material transitions can help mitigate the impact of these abrupt property changes. This approach is becoming increasingly accessible through three-dimensional (3D) printing, but it has yet to be extensively studied for rapid prototyping processes that are limited in resolution or number of material types. This study aims to investigate methods for applying graded transitions when using manufacturing processes with these limitations.

This study introduces a series of transition types that have graded properties and are produced using a finite number of discrete materials. This study presents a workflow for generating, fabricating and testing these transition types. This study uses this workflow with two different manufacturing processes to characterize the impact of each transition type on the ultimate tensile strength of a component.

Graded transitions can improve the performance of a component if the proper transition type is used. For high-fidelity processes, the best performing transitions are those closest to a true gradient. For low-fidelity processes, the best performing transitions are those which provide a balance of graded properties and mechanical connection.

The presented performance trends are specific to the studied processes and materials. Future work using different fabrication parameters can use the presented workflow to assess process-specific trends.

This work comprehensively compares different methods of creating graded transitions using discrete materials, including several novel approaches. It also provides a new design workflow that allows the design of graded transitions to be easily integrated into a 3D printing workflow.

The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to the complex patterns of color-patterned fabrics. The purpose of this paper is to propose a defect detection framework based on unsupervised adversarial learning for image reconstruction to solve the above problems.

The proposed framework consists of three parts: a generator, a discriminator and an image postprocessing module. The generator is able to extract the features of the image and then reconstruct the image. The discriminator can supervise the generator to repair defects in the samples to improve the quality of image reconstruction. The multidifference image postprocessing module is used to obtain the final detection results of color-patterned fabric defects.

The proposed framework is compared with state-of-the-art methods on the public dataset YDFID-1(Yarn-Dyed Fabric Image Dataset-version1). The proposed framework is also validated on several classes in the MvTec AD dataset. The experimental results of various patterns/classes on YDFID-1 and MvTecAD demonstrate the effectiveness and superiority of this method in fabric defect detection.

It provides an automatic defect detection solution that is convenient for engineering applications for the inspection process of the color-patterned fabric manufacturing industry. A public dataset is provided for academia.

While additive manufacturing via melt-extrusion of plastics has been around for more than several decades, its application to complex geometries has been hampered by the discretization of parts into planar layers. This requires wasted support material and introduces anisotropic weaknesses due to poor layer-to-layer adhesion. Curved-layer manufacturing has been gaining attention recently, with increasing potential to fabricate complex, low-weight structures, such as mechanical metamaterials. This paper aims to study the fabrication and mechanical characterization of non-planar lattice structures under cyclic loading.

A mathematical approach to parametrize lattices onto Bèzier surfaces is validated and applied here to fabricate non-planar lattice samples via curved-layer fused deposition modeling. The lattice chirality, amplitude and unit cell size were varied, and the properties of the samples under cyclic-loading were studied experimentally.

Overall, lattices with higher auxeticity showed less energy dissipation, attributed to their bending-deformation mechanism. Additionally, bistability was eliminated with increasing auxeticity, reinforcing the conclusion of bending-dominated behavior. The analysis presented here demonstrates that mechanical metamaterial lattices such as auxetics can be explored experimentally for complex geometries where traditional methods of comparing simple geometry to end-use designs are not applicable.

The mechanics of non-planar lattice structures fabricated using curved-layer additive manufacturing have not been studied thoroughly. Furthermore, traditional approaches do not apply due to parameterization deformations, requiring novel approaches to their study. Here the properties of such structures under cyclic-loading are studied experimentally for the first time. Applications for this type of structures can be found in areas like biomedical scaffolds and stents, sandwich-panel packaging, aerospace structures and architecture of lattice domes.

This work presents an experimental approach to study the mechanical properties of non-planar lattice structures via quasi-static cyclic loading, comparing variations across several lattice patterns including auxetic sinusoids, disrupted sinusoids and their equivalent-density quadratic patterns.

The purpose of this paper is to introduce, for the first time, the topology optimization method into the lattice structure configuration design for rapid casting patterns.

A structural topology optimization procedure in combination with thermo‐mechanical finite element analysis for the lattice structure configuration design has been developed.

A new mixed stress‐compliance optimization model is proposed for the strength and rigidity design. Numerical modeling about the mathematical formulation of the objective function and design constraints is established and an optimal material layout inside a given domain of the stereolithography (SL) resin pattern is found.

Various optimal results of lattice structure configurations are obtained numerically. By comparing the optimal designs with the existing lattice structure configurations, the newly obtained designs have shown better performances both in reducing the stress in the ceramic shell and in maintaining the stiffness of the SL pattern.

With the explosive growth of information available on the World Wide Web, it has become much more difficult to access relevant information from the Web. One possible approach to solve this problem is web personalization. In this paper, we propose a novel WUL (Web Usage Lattice) based mining approach for mining association access pattern rules for personalized web recommendations. The proposed approach aims to mine a reduced set of effective association pattern rules for enhancing the online performance of web recommendations. We have incorporated the proposed approach into a personalized web recommender system known as AWARS. The performance of the proposed approach is evaluated based on the efficiency and the quality. In the efficiency evaluation, we measure the number of generated rules and the runtime for online recommendations. In the quality evaluation, we measure the quality of the recommendation service based on precision, satisfactory and applicability. This paper will discuss the proposed WUL‐based mining approach, and give the performance of the proposed approach in comparison with the Apriori‐based algorithms.

Pattern design is a transformation process from the design sketch to the final production pattern. In order to computerize such a process, a mathematical model is indispensable. When a garment is decomposed into components of pattern pieces, this decomposition process can be modelled by using semi‐group. Presents the construction of this semi‐group structure, which is called the abelian pattern semi‐group, and the associated lattice structure with proofs and examples. Illustrates the interpretation and application of this algebraic model with the optimal control problem of the production line.

Natural collective phenomena, for example, the movement of crowds of pedestrians and the impressive nest formations of social insects, provide us with an existence proof that sophisticated constructions may be built by swarms of relatively simple artificial agents. The constructions often appear to have required impressive control and coordination – yet each agent in the collective does not appear to be provided with an internal world model or blue‐print for the complete construction. These macroscopic structures emerge as the consequence of interaction of agents, carrying out simple rules based upon the local state of the world, which includes the interaction between agents and the growing structure. In an attempt to understand the underpinning principles of structure formation in collectives of minimal mobile agents the paper focuses on an investigation of automata‐like agents in a two‐dimensional lattice. All agents start their evolution at the same site on the lattice. Every agent moves at random until it finds a neighbourhood it likes more than other neighbourhoods. The agents form a stationary structure of their immobile bodies. The paper focuses upon the parameterisation of the rule space and the mapping between parameter space and the resulting global structure formed by the agents.

The quality of lost foam casted engineering components is directly influenced by the characteristics of the respective ceramic shell mold (CSM) and hence casting pattern. In this present work, rapid prototyping (RP) was used to fabricate the lattice structured patterns (LSPs) to reduce the defects and cracks in CSM during the heating stage.

The quality of the LSPs was accessed by measuring the dimensional accuracy. Further, the thermal stress in the CSM during the heating of porosity varied LSPs was analyzed using ANSYS software package 16.0. The Ni-alloy casting was fabricated by using the designed LSP and compared with its respective CAD model to access its quality.

The obtained results revealed that the Wigner–Seitz LSPs retained high accuracy and minimized the stress for defect-free CSM. Also, the thermal stress generated in the CSM depends upon the porosity coefficient of the LSP. Hence the interplay with porosity coefficient of LSPs leads to the formation of defect free CSM and hence high quality casting.

RP was used to develop LSPs and investigated the dependency of unit cell parameters on the accuracy of the final casting.