Rapid prototyping (RP) models are no longer used only for design verification. Currently, parts built utilizing layer manufacturing technology can be employed as functional prototypes and as patterns or tools for different manufacturing processes such as vacuum casting, investment casting, injection moulding, die casting and sand casting. The selective laser sintering (SLS) process is becoming widely used in industry, its main advantage over other RP processes being that it can produce parts in a wide range of materials. This paper describes applications of some of the materials available with the SLS process and, by means of case studies, explains the technological capabilities of this process.

Laser powder bed fusion (LPBF) is a proven additive manufacturing (AM) technology for producing metallic components with complex shapes using layer-by-layer manufacture principle. However, the fabrication of crack-free high-performance Nibased superalloys such as Hastelloy X (HX) using LPBF technology remains a challenge because of these materials' susceptibility to hot cracking. This paper addresses the above problem by proposing a novel method of introducing 1 wt.% titanium carbide (TiC) nanoparticles. The findings reveal that the addition of TiC nanoparticles results in the elimination of microcracks in the LPBF-fabricated enhanced HX samples; i.e. the 0.65% microcracks that were formed in the asfabricated original HX were eliminated in the as-fabricated enhanced HX, despite the 0.14% residual pores formed. It also contributes to a 21.8% increase in low-angle grain boundaries (LAGBs) and a 98 MPa increase in yield strength. The study revealed that segregated carbides were unable to trigger hot cracking without sufficient thermal residual stresses; the significantly increased subgrains and lowangle grain boundaries played a key role in the hot cracking elimination. These findings offer a new perspective on the elimination of hot cracking of nickel-based superalloys and other industrially relevant crack-susceptible alloys. The findings also have significant implications for the design of new alloys, particularly for hightemperature industrial applications.

Metal Additive Manufacturing (AM) has been attracting a continuously increasing attention due to its great advantages compared to traditional subtractive manufacturing in terms of higher design flexibility, shorter development time, lower tooling cost, and fewer production wastes. However, the lack of process robustness, stability and repeatability caused by the unsolved complex relationships between material properties, product design, process parameters, process signatures, post AM processes and product quality has significantly impeded its broad acceptance in the industry. To facilitate efficient implementation of advanced data analytics in metal AM, which would support the development of intelligent process monitoring, control and optimisation, this paper proposes a novel Digital Twin (DT)-enabled collaborative data management framework for metal AM systems, where a Cloud DT communicates with distributed Edge DTs in different product lifecycle stages. A metal AM product data model that contains a comprehensive list of specific product lifecycle data is developed to support the collaborative data management. The feasibility and advantages of the proposed framework are validated through the practical implementation in a distributed metal AM system developed in the project MANUELA. A representative application scenario of cloud-based and deep learning-enabled metal AM layer defect analysis is also presented. The proposed DT-enabled collaborative data management has shown great potential in enhancing fundamental understanding of metal AM processes, developing simulation and prediction models, reducing development times and costs, and improving product quality and production efficiency.

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