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Digital twin (DT) is an emerging technology that enables sophisticated interaction between physical objects and their virtual replicas. Although DT has recently gained significant attraction in both industry and academia, there is no systematic understanding of DT from its development history to its different concepts and applications in disparate disciplines. The majority of DT literature focuses on the conceptual development of DT frameworks for a specific implementation area. Hence, this paper provides a state-of-the-art review of DT history, different definitions and models, and six types of key enabling technologies. The review also provides a comprehensive survey of DT applications from two perspectives: (1) applications in four product-lifecycle phases, i.e. product design, manufacturing, operation and maintenance, and recycling and (2) applications in four categorized engineering fields, including aerospace engineering, tunneling and underground engineering, wind engineering and Internet of things (IoT) applications. DT frameworks, characteristic components, key technologies and specific applications are extracted for each DT category in this paper. A comprehensive survey of the DT references reveals the following findings: (1) The majority of existing DT models only involve one-way data transfer from physical entities to virtual models and (2) There is a lack of consideration of the environmental coupling, which results in the inaccurate representation of the virtual components in existing DT models. Thus, this paper highlights the role of environmental factor in DT enabling technologies and in categorized engineering applications. In addition, the review discusses the key challenges and provides future work for constructing DTs of complex engineering systems.

Honeycombs enjoy wide use in various engineering applications. The emergence of additive manufacturing (AM) as a method of customisable of parts has enabled the reinvention of the honeycomb structure. However, research on in-plane compressive performance of both classical and new types of honeycombs fabricated via AM is still ongoing. Several important findings have emerged over the past years, with significance for the AM community and a review is considered necessary and timely. This paper aims to review the in-plane compressive performance of AM honeycomb structures.

This paper provides a state-of-the-art review focussing on the in-plane compressive performance of AM honeycomb structures, covering both polymers and metals. Recently published studies, over the past six years, have been reviewed under the specific theme of in-plane compression properties.

The key factors influencing the AM honeycombs' in-plane compressive performance are identified, namely the geometrical features, such as topology shape, cell wall thickness, cell size and manufacturing parameters. Moreover, the techniques and configurations commonly used for geometry optimisation toward improving mechanical performance are discussed in detail. Current AM limitations applicable to AM honeycomb structures are identified and potential future directions are also discussed in this paper.

This work evaluates critically the primary results and findings from the published research literature associated with the in-plane compressive mechanical performance of AM honeycombs.

Effective rail surface defects detection method is the basic guarantee to manufacture high-quality rail. However, the existed visual inspection methods have disadvantages such as poor ability to locate the rail surface region and high sensitivity to uneven reflection. This study aims to propose a bionic rail surface defect detection method to obtain the high detection accuracy of rail surface defects under uneven reflection environments.

Through this bionic rail surface defect detection algorithm, the positioning and correction of the rail surface region can be computed from maximum run-length smearing (MRLS) and background difference. A saliency image can be generated to simulate the human visual system through some features including local grayscale, local contrast and edge corner effect. Finally, the meanshift algorithm and adaptive threshold are developed to cluster and segment the saliency image.

On the constructed rail defect data set, the bionic rail surface defect detection algorithm shows good recognition ability on the surface defects of the rail. Pixel- and defect-level index in the experimental results demonstrate that the detection algorithm is better than three advanced rail defect detection algorithms and five saliency models.

The bionic rail surface defect detection algorithm in the production process is proposed. Particularly, a method based on MRLS is introduced to extract the rail surface region and a multifeature saliency fusion model is presented to identify rail surface defects.

The purpose of this paper is to present a Design for Additive Manufacturing (DfAM) methodology that connects several methods, from geometrical design to post-process selection, into a common optimisation framework.

A design methodology is formulated and tested in a case study. The outcome of the case study is analysed by comparing the obtained results with alternative designs achieved by using other design methods. The design process in the case study and the potential of the method to be used in different settings are also discussed. Finally, the work is concluded by stating the main contribution of the paper and highlighting where further research is needed.

The proposed method is implemented in a novel framework which is applied to a physical component in the case study. The component is a structural aircraft part that was designed to minimise weight while respecting several static and fatigue structural load cases. An addition goal is to minimise the manufacturing cost. Designs optimised for manufacturing by two different AM machines (EOS M400 and Arcam Q20+), with and without post-processing (centrifugal finishing) are considered. The designs achieved in this study show a significant reduction in both weight and cost compared to one AM manufactured geometry designed using more conventional methods and one design milled in aluminium.

The method in this paper allows for the holistic design and optimisation of components while considering manufacturability, cost and component functionality. Within the same framework, designs optimised for different setups of AM machines and post-processing can be automatically evaluated without any additional manual work.

This paper aims to present a comprehensive review of the laser engineered net shaping (LENS) process in an attempt to provide the reader with a deep understanding of the controllable and fixed build parameters of metallic parts. The authors discuss the effect and interplay between process parameters, including: laser power, scan speed and powder feed rate. Further, the authors show the interplay between process parameters is pivotal in achieving the desired microstructure, macrostructure, geometrical accuracy and mechanical properties.

In this manuscript, the authors review current research examining the process inputs and their influences on the final product when manufacturing with the LENS process. The authors also discuss how these parameters relate to important build aspects such as melt-pool dimensions, the volume of porosity and geometry accuracy.

The authors conclude that studies have greatly enriched the understanding of the LENS build process, however, much studies remains to be done. Importantly, the authors reveal that to date there are a number of detailed theoretical models that predict the end properties of deposition, however, much more study is necessary to allow for reasonable prediction of the build process for standard industrial parts, based on the synchronistic behavior of the input parameters.

This paper intends to raise questions about the possible research areas that could potentially promote the effectiveness of this LENS technology.

Currently on additive manufacturing, extensive research is directed toward mitigating the main challenges associated with multi-material in fused filament fabrication which has a weak bonding strength between dissimilar materials. Low interfacial bonding strength leads to defects, anisotropy and temperature gradient in materials which negatively impact the mechanical performance of the multi-material prints. The purpose of this study was to assess the performance of different interface geometry designs in terms of the mechanical properties of the specimens.

Tensile test specimens were printed using: mono-material without a boundary interface, mono-material with the interface geometries (Face-to-face; U-shape; T-shape; Dovetail; Encapsulation; Mechanical interlocking; and Overlap) and multi-material with the interface geometries. The materials chosen with high and low compatibility were Tough polylactic acid (PLA) and TPU.

The main results of this study indicate that the interface geometries with the mechanical constriction between materials provide better structural integrity to the specimens. Moreover, in the case of the mono-material parts, the most effective interface design was the mechanical interlocking for both Tough PLA and TPU. On the other hand, in the case of multi-material specimens, the encapsulation showed the highest ultimate tensile strength, whereas the overlap and T-shape presented more robust bonding.

This study examines the mechanical performance, particularly tensile strength, strain at break, Young’s modulus and yield strength of different interface designs which were not studied in the previous studies.

Laser sintering of polyamide lattice-based lightweight fairing components for subsequent racetrack testing requires a high quality and a reliable design. Hence, the purpose of this study was to develop a design methodology for such additively manufactured prototypes, considering efficient generation and structural simulation of boundary conformal non-periodic lattices, optimization of production parameters as well as experimental validation.

Multi-curved, sandwich structure-based demonstrators were designed, simulated and experimentally tested with boundary conformal lattice cells. The demonstrator’s non-periodic lattice cells were simplified by forward homogenization processes. To represent the stiffness of the top and bottom face sheet, constant isotropic and mapped transversely isotropic simulation approaches were compared. The dimensional accuracy of lattice cells and demonstrators were measured with a gauge caliper and a three-dimensional scanning system. The optimized process parameters for lattice structures were transferred onto a large volume laser sintering system. The stiffness of each finite element analysis was verified by an experimental test setup including a digital image correlation system.

The stiffness prediction of the mapped was superior to the constant approach and underestimated the test results with −6.5%. Using a full scale fairing the applicability of the development process was successfully demonstrated.

The design approach elaborated in this research covers aspects from efficient geometry generation over structural simulation to experimental testing of produced parts. This methodology is not only relevant in the context of motor sports but is transferrable for all additively manufactured large scale components featuring a complex lattice sub-structure and is, therefore, relevant across industries.

This research aims to improve the performance of rail fastener defect inspection method for multi railways, to effectively ensure the safety of railway operation.

Firstly, a fastener region location method based on online learning strategy was proposed, which can locate fastener regions according to the prior knowledge of track image and template matching method. Online learning strategy is used to update the template library dynamically, so that the method not only can locate fastener regions in the track images of multi railways, but also can automatically collect and annotate fastener samples. Secondly, a fastener defect recognition method based on deep convolutional neural network was proposed. The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region. The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.

Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways. Specifically, fastener location module has achieved an average detection rate of 99.36%, and fastener defect recognition module has achieved an average precision of 96.82%.

The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways, which has high reliability and strong adaptability to multi railways.

The rostrum of a paddlefish provides hydrodynamic stability during feeding process in addition to detect the food using receptors that are randomly distributed in the rostrum. The exterior tissue of the rostrum covers the cartilage that surrounds the bones forming interlocking star shaped bones.

The aim of this work is to assess the mechanical behavior of four finite element models varying the type of formulation as follows: linear-reduced integration, linear-full integration, quadratic-reduced integration and quadratic-full integration. The paper also presents the load transfer mechanisms of the bone structure of the rostrum. The base material used in the study was steel with elastic–plastic behavior as a homogeneous material before applying materials properties that represents the behavior of bones, cartilages and tissues.

Conclusions are based on comparison among the four models. There is no significant difference between integration orders for similar type of elements. Quadratic-reduced integration formulation resulted in lower structural stiffness compared with linear formulation as seen by higher displacements and stresses than using linearly formulated elements. It is concluded that second-order elements with reduced integration are the alternative to analyze biological structures as they can better adapt to the complex natural contours and can model accurately stress concentrations and distributions without over stiffening their general response.

The use of advanced computational mechanics techniques to analyze the complex geometry and components of the paddlefish rostrum provides a viable avenue to gain fundamental understanding of the proper finite element formulation needed to successfully obtain the system behavior and hot spot locations.

This study aims to propose a vertical coupling dynamic analysis method of vehicle–track–substructure based on forced vibration and use this method to analyze the influence on the dynamic response of track and vehicle caused by local fastener failure.

The track and substructure are decomposed into the rail subsystem and substructure subsystem, in which the rail subsystem is composed of two layers of nodes corresponding to the upper rail and the lower fastener. The rail is treated as a continuous beam with elastic discrete point supports, and spring-damping elements are used to simulate the constraints between rail and fastener. Forced displacement and forced velocity are used to deal with the effect of the substructure on the rail system, while the external load is used to deal with the reverse effect. The fastener failure is simulated with the methods that cancel the forced vibration transmission, namely take no account of the substructure–rail interaction at that position.

The dynamic characteristics of the infrastructure with local diseases can be accurately calculated by using the proposed method. Local fastener failure will slightly affect the vibration of substructure and carbody, but it will significantly intensify the vibration response between wheel and rail. The maximum vertical displacement and the maximum vertical vibration acceleration of rail is 2.94 times and 2.97 times the normal value, respectively, under the train speed of 350 km·h⁻¹. At the same time, the maximum wheel–rail force and wheel load reduction rate increase by 22.0 and 50.2%, respectively, from the normal value.

This method can better reveal the local vibration conditions of the rail and easily simulate the influence of various defects on the dynamic response of the coupling system.