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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.

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.