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Additive manufacturing (AM) enables the fabrication of complex geometries beyond the capability of traditional manufacturing methods. Complex lattice structures have enabled engineering innovation; however, the use of traditional computer-aided design (CAD) methods for the generation of lattice structures is inefficient, time-consuming and can present challenges to process integration. In an effort to improve the implementation of lattice structures into engineering applications, this paper aims to develop a programmatic lattice generator (PLG).

The PLG method is computationally efficient; has direct control over the quality of the stereolithographic (STL) file produced; enables the generation of more complex lattice than traditional methods; is fully programmatic, allowing batch generation and interfacing with process integration and design optimization tools; capable of generating a lattice STL file from a generic input file of node and connectivity data; and can export a beam model for numerical analysis.

This method has been successfully implemented in the generation of uniform, radial and space filling lattices. Case studies were developed which showed a reduction in processing time greater than 60 per cent for a 3,375 cell lattice over traditional CAD software.

The PLG method is a novel design for additive manufacture (DFAM) tool with unique advantages, including full control over the number of facets that represent a lattice strut, allowing optimization of STL data to minimize file size, while maintaining suitable resolution for the implemented AM process; programmatic DFAM capability that overcomes the learning curve of traditional CAD when producing complex lattice structures, therefore is independent of designer proficiency and compatible with process integration; and the capability to output both STL files and associated data for numerical analysis, a unique DFAM capability not previously reported.

This study aims to comprehensively investigate the electron beam powder bed fusion (EB-PBF) process for copper, offering validated estimations of melt pool temperature and morphology through numerical and analytical approaches. This work also assesses how process parameters influence the temperature fluctuations and the morphological changes of the melt pool.

Two distinct methods, an analytical model and a numerical simulation, were used to assess temperature profiles, melt pool morphology and associated heat transfer mechanisms, including conduction and keyhole mode. The analytical model considers conduction as the dominant heat transfer mechanism; the numerical model also includes convection and radiation, incorporating specific parameters such as beam power, scan speed, thermophysical material properties and powder interactions.

Both the analytical model and numerical simulations are highly correlated. Results indicated that the analytical model, emphasising material conduction, exhibited exceptional precision, although at substantially reduced cost. Statistical analysis of numerical outcomes underscored the substantial impact of beam power and scan speed on melt pool morphology and temperature in EB-PBF of copper.

This numerical simulation of copper in EB-PBF is the first high-fidelity model to consider the interaction between powder and substrate comprehensively. It accurately captures material properties, powder size distribution, thermal dynamics (including heat transfer between powder and substrate), phase changes and fluid dynamics. The model also integrates advanced computational methods such as computational fluid dynamics and discrete element method. The proposed model and simulation offer a valuable predictive tool for melt pool temperature, heat transfer processes and morphology. These insights are critical for ensuring the bonding quality of subsequent layers and, consequently, influencing the overall quality of the printed parts.

Metal additive manufacturing is an inherently thermal process, with intense localised heating and for sparse lattice structures, often rapid uneven cooling. Thermal effects influence manufactured geometry through residual stresses and may also result in non-isotropic material properties. This paper aims to increase understanding of the evolution of the temperature field during fabrication of lattice structures through numerical simulation.

This paper uses a reduced order numerical analysis based on “best-practice” compromise found in literature to explore design permutations for lattice structures and provide first-order insight into the effect of these design variables on the temperature field.

Instantaneous and peak temperatures are examined to discover trends at select lattice locations. Insights include the presence of vertical struts reduces overall lattice temperatures by providing additional heat transfer paths; at a given layer, the lower surface of an inclined strut experiences higher temperatures than the upper surface throughout the fabrication of the lattice; during fabrication of the lower layers of the lattice, isolated regions of material can experience significantly higher temperatures than adjacent regions.

Due to the simplifying assumptions and multi-layer material additions, the findings are qualitative in nature. Future research should incorporate additional heat transfer mechanisms.

These findings point towards thermal differences within the lattice which may manifest as dimensional differences and microstructural changes in the built part.

The paper provides qualitative insights into the effect of local geometry and topology upon the evolution of temperature within lattice structures fabricated in metal additive manufacturing.

Fused deposition modelling (FDM) is increasingly being explored as a commercial fabrication method due to its ability to produce net or near-net shape parts directly from a computer-aided design model. Other benefits of technology compared to conventional manufacturing include lower cost for short runs, reduced product lead times and rapid product design. High-performance polymers such as polyetherimide, have the potential for FDM fabrication and their high-temperature capabilities provide the potential of expanding the applications of FDM parts in automotive and aerospace industries. However, their relatively high glass transition temperature (215 °C) causes challenges during manufacturing due to the requirement of high-temperature build chambers and controlled cooling rates. The purpose of this study is to investigate the mechanical properties of ULTEM 1010, an unfilled polyetherimide grade.

In this research, mechanical properties were evaluated through tensile and flexural tests. Analysis of variance was used to determine the significance of process parameters to the mechanical properties of the specimens, their main effects and interactions. The fractured surfaces were analysed by scanning electron microscopy and optical microscopy and porosity was assessed by X-ray microcomputed tomography.

A range of mean tensile and flexural strengths, 60–94 MPa and 62–151 MPa, respectively, were obtained highlighting the dependence of performance on process parameters and their interactions. The specimens were found to fracture in a brittle manner. The porosity of tensile samples was measured between 0.18% and 1.09% and that of flexural samples between 0.14% and 1.24% depending on the process parameters. The percentage porosity was found to not directly correlate with mechanical performance, rather the location of those pores in the sample.

This analysis quantifies the significance of the effect of each of the examined process parameters has on the mechanical performance of FDM-fabricated specimens. Further, it provides a better understanding of the effect process parameters and their interactions have on the mechanical properties and porosity of FDM-fabricated polyetherimide specimens. Additionally, the fracture surface of the tested specimens is qualitatively assessed.

This paper aims to present a methodology for the detection and categorisation of metal powder particles that are partially attached to additively manufactured lattice structures. It proposes a software algorithm to process micro computed tomography (µCT) image data, thereby providing a systematic and formal basis for the design and certification of powder bed fusion lattice structures, as is required for the certification of medical implants.

This paper details the design and development of a software algorithm for the analysis of µCT image data. The algorithm was designed to allow statistical probability of results based on key independent variables. Three data sets with a single unique parameter were input through the algorithm to allow for characterisation and analysis of like data sets.

This paper demonstrates the application of the proposed algorithm with three data sets, presenting a detailed visual rendering derived from the input image data, with the partially attached particles highlighted. Histograms for various geometric attributes are output, and a continuous trend between the three different data sets is highlighted based on the single unique parameter.

This paper presents a novel methodology for non-destructive algorithmic detection and categorisation of partially attached metal powder particles, of which no formal methods exist. This material is available to download as a part of a provided GitHub repository.

This paper reviews the synergy of Industry 4.0 and additive manufacturing (AM) and discusses the integration of data-driven manufacturing systems and product service systems as a key component of the Industry 4.0 revolution. This paper aims to highlight the potential effects of Industry 4.0 on AM via tools such as digitalisation, data transfer, tagging technology, information in Industry 4.0 and intelligent features.

In successive phases of industrialisation, there has been a rise in the use of, and dependence on, data in manufacturing. In this review of Industry 4.0 and AM, the five pillars of success that could see the Internet of Things (IoT), artificial intelligence, robotics and materials science enabling new levels of interactivity and interdependence between suppliers, producers and users are discussed. The unique effects of AM capabilities, in particular mass customisation and light-weighting, combined with the integration of data and IoT in Industry 4.0, are studied for their potential to support higher efficiencies, greater utility and more ecologically friendly production. This research also illustrates how the digitalisation of manufacturing for Industry 4.0, through the use of IoT and AM, enables new business models and production practices.

The discussion illustrates the potential of combining IoT and AM to provide an escape from the constraints and limitations of conventional mass production whilst achieving economic and ecological savings. It should also be noted that this extends to the agile design and fabrication of increasingly complex parts enabled by simulations of complex production processes and operating systems. This paper also discusses the relationship between Industry 4.0 and AM with respect to improving the quality and robustness of product outcomes, based on real-time data/feedback.

This research shows how a combined approach to research into IoT and AM can create a step change in practice that alters the production and supply paradigm, potentially reducing the ecological impact of industrial systems and product life cycle. This paper demonstrates how the integration of Industry 4.0 and AM could reshape the future of manufacturing and discusses the challenges involved.

Biofouling of marine vessels results in significant operational costs, as well as the bio-security risk associated with the transport of marine pests. Biofouling is particularly rapid in sea-chest water intakes due to elevated temperatures and circulating flow. Inspection challenges are exacerbated, as sea chests are difficult to inspect and clean. This paper aims to present a method that utilises the flexibility and low-batch capabilities of additive manufacture to manufacture custom sea-chest inserts that eliminate circulating flow and increase the uniformity of shear stress distributions to enable more constant ablation of anti-biofouling coatings.

An automated design procedure has been developed to optimise sea-chest insert geometry to achieve desirable flow characteristics, while eliminating the necessity for support material in FDM manufacture – thereby significantly reducing build cost and time.

Numerical flow simulation confirms that the fluid-flow approximation is robust for optimising sea-chest insert geometry. Insert geometry can be manipulated to enable support-free additive manufacture; however, as the threshold angle for support-free manufacture increases, the set of feasible sea-chest aspect ratios decreases.

The surface of revolution that defines the optimal insert geometry may result in features that are not compatible with additive manufacture constraints. An alternate geometry is proposed that may be more useful in practice without compromising anti-biofouling properties.

Marine sea-chest biofouling results in significant negative environmental and economic consequence. The method developed in this paper can reduce the negative impact of sea-chest biofouling.

Marine sea-chest biofouling results in significant resource consumption and emissions. The method developed in this paper can reduce the negative impact of sea-chest biofouling.

The method presented in this paper provides an entirely original opportunity to utilise additive manufacture to mitigate the effects of marine biofouling.

Additive manufacture (AM) such as selective laser melting (SLM) provides significant geometric design freedom in comparison with traditional manufacturing methods. Such freedom enables the construction of injection moulding tools with conformal cooling channels that optimize heat transfer while incorporating efficient internal lattice structures that can ground loads and provide thermal insulation. Despite the opportunities enabled by AM, there remain a number of design and processing uncertainties associated with the application of SLM to injection mould tool manufacture, in particular from H13/DIN 1.2344 steel as commonly used in injection moulds. This paper aims to address several associated uncertainties.

A number of physical and numerical experimental studies are conducted to quantify SLM-manufactured H13 material properties, part manufacturability and part characteristics.

Findings are presented which quantify the effect of SLM processing parameters on the density of H13 steel components; the manufacturability of standard and self-supporting conformal cooling channels, as well as structural lattices in H13; the surface roughness of SLM-manufactured cooling channels; the effect of cooling channel layout on the associated stress concentration factor and cooling uniformity; and the structural and thermal insulating properties of a number of structural lattices.

The contributions of this work with regards to SLM manufacture of H13 of injection mould tooling can be applied in the design of conformal cooling channels and lattice structures for increased thermal performance.

The purpose of this paper is to understand how Design for Additive manufacturing Knowledge has been developing and its significance to both academia and industry.

In this paper, the authors use a bibliometric approach to analyse publications from January 2010 to December 2020 to explore the subject areas, publication outlets, most active authors, geographical distribution of scholarly outputs, collaboration and co-citations at both institutional and geographical levels and outcomes from keywords analysis.

The findings reveal that most knowledge has been developed in DfAM methods, rules and guidelines. This may suggest that designers are trying to learn new ways of harnessing the freedom offered by AM. Furthermore, more knowledge is needed to understand how to tackle the inherent limitations of AM processes. Moreover, DfAM knowledge has thus far been developed mostly by authors in a small number of institutional and geographical clusters, potentially limiting diverse perspectives and synergies from international collaboration which are essential for global knowledge development, for improvement of the quality of DfAM research and for its wider dissemination.

A concise structure of DfAM knowledge areas upon which the bibliometric analysis was conducted has been developed. Furthermore, areas where research is concentrated and those that require further knowledge development are revealed.

This study assesses the image of Singapore as a tourist destination, using a conceptual model that combines the concepts of the service quality framework and the likelihood of revisiting and recommendation. Four hundred and thirty-one tourists departing from Singapore Changi International Airport were interviewed using a structured questionnaire. Factor analysis was first used to reduce the 25 variables to 5 new variables. It was found that all new variables are significant in affecting the overall satisfaction levels regardless of whether they were Asian or non-Asian groups. In addition to these findings, it was also found that the overall satisfaction did lead to their likelihood of revisiting and recommendation to their relatives to visit Singapore.