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Lattice structures are broadly used in lightweight structure designs and multifunctional applications. Especially, with the unprecedented capabilities of additive manufacturing (AM) technologies and computational optimization methods, design of nonuniform lattice structures has recently attracted great research interests. To eliminate constraints of the common "ground structure approaches" (GSAs), a novel topology optimization-based method is proposed in this paper. Particularly, the structural wall thickness in the proposed design method was set as uniform for better manufacturability. As a solution to carry out the optimized material distribution for the lattice structure, geometrical size of each unit cell was set as design variable. The relative density model, which can be obtained from the solid isotropic microstructure with penalization (SIMP)-based topology optimization method, was mapped into a nonuniform lattice structure with different size cells. Finite element analysis (FEA)-based homogenization method was applied to obtain the mechanical properties of these different size gradient unit cells. With similar mechanical properties, elements with different "relative density" were translated into unit cells with different size. Consequently, the common topology optimization result can be mapped into a nonuniform lattice structure. This proposed method was computationally and experimentally validated by two different load-support design cases. Taking advantage of the changeable surface-to-volume ratio through manipulating the cell size, this method was also applied to design a heat sink with optimum heat dissipation efficiency. Most importantly, this design method provides a new perspective to design nonuniform lattice structures with enhanced functionality and manufacturability. [ABSTRACT FROM AUTHOR]

Design documents and design project footprints accumulated by corporate information technology systems have increasingly become valuable sources of evidence for design information and knowledge management. Identification and extraction of such embedded information and knowledge into a clear and usable format will greatly accelerate continuous learning from past design efforts for competitive product innovation and efficient design process management in future design projects. Most of the existing design information extraction systems focus on either organizing design documents for efficient retrieval or extracting relevant product information for product optimization. Different from traditional systems, this paper proposes a methodology of learning and extracting useful knowledge using past design project documents from design process perspective based on process mining techniques. Particularly different from conventional techniques that deal with timestamps or event logs only, a new process mining approach that is able to directly process textual data is proposed at the first stage of the proposed methodology. The outcome is a hierarchical process model that reveals the actual design process hidden behind a large amount of design documents and enables the connection of various design information from different perspectives. At the second stage, the discovered process model is analyzed to extract multifaceted knowledge patterns by applying a number of statistical analysis methods. The outcomes range from task dependency study from workflow analysis, identification of irregular task execution from performance analysis, cooperation pattern discovery from social net analysis to evaluation of personal contribution based on role analysis. Relying on the knowledge patterns extracted, lessons and best practices can be uncovered which offer great support to decision makers in managing any future design initiatives. The proposed methodology was tested using an email dataset from a university-hosted multiyear multidisciplinary design project. [ABSTRACT FROM AUTHOR]

BACKGROUND: The long-term prognosis of patients with hepatocellular carcinoma (HCC) after surgery remains far from satisfactory, especially in patients with microvascular invasion (MVI). This study aimed to evaluate the potential survival benefit from adjuvant lenvatinib for patients with HCC and MVI. METHODS: Patients with HCC after curative hepatectomy were reviewed. All patients were divided into 2 groups according to adjuvant lenvatinib. Propensity score matching (PSM) analysis was used to reduce selection bias and make the results more robust. Survival curves are shown by the Kaplan-Meier (K-M) analysis and compared by the Log-rank test. Univariate and multivariate Cox regression analyses were performed to determine the independent risk factors. RESULTS: Of 179 patients enrolled in this study, 43 (24%) patients received adjuvant lenvatinib. After PSM analysis, 31 pairs of patients were enrolled for further analysis. Survival analysis before and after PSM analysis showed a better prognosis in the adjuvant lenvatinib group (all P < .05). The adverse events associated with oral lenvatinib were acceptable. Multivariate Cox regression analysis showed that adjuvant lenvatinib was an independent protective factor for improving overall survival (OS) (hazard ratio [HR] = 0.455, 95% confidence interval [CI] = 0.249-0.831, P = .001) and recurrence-free survival (RFS) (HR = 0.523, 95% CI = 0.308-0.886, P = .016). CONCLUSIONS: Postoperative adjuvant targeted therapy can improve the long-term prognosis of patients with HCC and MVI. Therefore, in clinical practice, oral lenvatinib should be recommended for patients with HCC and MVI to decrease tumor recurrence and improve long-term survival. [ABSTRACT FROM AUTHOR]

With the prosperity of laser powder bed fusion (LPBF) technology, post-processing tends to be increasingly attractive in further improving the performance of the LPBFed parts. In this study, post-heat treatment and electroless plating were performed on the LPBFed 316L stainless steel to investigate the evolution of surface topography, mechanical property and corrosion behaviour. The results show that the as-built part features melt tracks and fine cellular substructures, which presents higher deformation resistance than the cast and recrystallized counterparts. The uniform amorphous Ni-P coating can significantly increase microhardness of as-built 316L stainless steel from ~300 to ~800 HV. In addition, the as-built parts with/without coatings have better corrosion resistance than the corresponding cast ones, which can be further improved through 450°C annealing. Moreover, the coatings can also prevent the localised corrosion effectively. Therefore, this study provides available post-processing methods for further improve the performance of LPBFed 316L stainless steel. [ABSTRACT FROM AUTHOR]

Additive manufacturing enables the fabrication of parts with complex geometries, thereby opening up the design space from part scale to microarchitecture scale. By optimising the structure in the expanded design space, structural performance can be improved. Topology optimisation is commonly used as the tool to optimise the structures according to specific application requirements. However, multiscale topology optimisation can be computationally expensive and with limited choices in microscale structures. Therefore, we propose a surrogate model based on three-dimensional convolutional neural networks (3D-CNN) to model the effective elasticity tensor and its gradients for general voxel-based nonparametric microstructures. The proposed 3D-CNN-based surrogate model greatly extends the flexibility over existing surrogate-based methods which can only be applied in relatively simple parametric microstructures. Given the microscale structure, the proposed 3D-CNN-based model can effectively predict its material properties. Furthermore, being able to estimate the gradient of the material properties with respect to microscale structure changes makes the proposed 3D-CNN-based surrogate readily adaptive to existing multiscale topology optimisation frameworks. Through extensive simulations, by comparing with both SIMP and existing surrogate-based methods, we demonstrate the advantages of the proposed 3D-CNN-based surrogate model. [ABSTRACT FROM AUTHOR]

Bioelectricity has been a fundamental property of all living organisms. With electrical stimulation, living cells can interact with their microenvironments, which makes electrical stimulation highly beneficial for various biomedical applications. However, traditional electrical stimulation mainly relies on bulky and complex equipment, which may not be ideal due to the restriction of movement, limited battery lifetime, uncomfortable wearing, and potential unfriendly to the environment. The advent of triboelectric nanogenerators (TENGs) has helped to resolve the existing limitations. TENGs are effective energy harvesting systems that use a mix of triboelectrification and electrostatic induction to create electrical energy from kinetic energy. TENGs deliver self-powered electrical stimulation to bone cells for functional regulation or bone regeneration, serve as sensors to detect biological signals or movements, or act as a power source for other biomedical devices. TENGs can be employed in various applications, including enhancing bone regeneration, providing sensing function, slowing bone aging, and curing implant-related infections. The recent applications of TENGs in bone tissue engineering are reviewed, and the drawbacks of the TENGs are discussed. Finally, the existing challenges and future roadmap for developing TENGs are presented. [ABSTRACT FROM AUTHOR]

The article introduces this issue with a selection of works from exhibitions and design competitions, highlighting designers such as Toshiyuki Kita and James Dyson.

Reports on an integrated process planning for simultaneous engineering product life-cycle management project at the Singapore Institute of Manufacturing Technology. Technical challenges for product life-cycle benefits; Overview of product life-cycle studies; Functions of life-cycle engineering as a field within product life-cycle management.

Nerve guidance conduits (NGCs) are tubular tissue engineering scaffolds used for nerve regeneration. The poor mechanical properties and porosity have always compromised their performances for guiding and supporting axonal growth. Therefore, in order to improve the properties of NGCs, the computational design approach was adopted to investigate the effects of different NGC structural features on their various properties, and finally, design an ideal NGC with mechanical properties matching human nerves and high porosity and permeability. Three common NGC designs, namely hollow luminal, multichannel, and microgrooved, were chosen in this study. Simulations were conducted to study the mechanical properties and permeability. The results show that pore size is the most influential structural feature for NGC tensile modulus. Multichannel NGCs have higher mechanical strength but lower permeability compared to other designs. Square pores lead to higher permeability but lower mechanical strength than circular pores. The study finally selected an optimized hollow luminal NGC with a porosity of 71% and a tensile modulus of 8 MPa to achieve multiple design requirements. The use of computational design and optimization was shown to be promising in future NGC design and nerve tissue engineering research. [ABSTRACT FROM AUTHOR]