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Laser powder bed fusion (LPBF) is a promising metal additive manufacturing technology for producing functional components. However, there are still a lot of obstacles to overcome before this technology is considered mature and trustworthy for wider industrial applications. One of the biggest obstacles is the difficulty in ensuring the repeatability of process and the reproducibility of products. To tackle this challenge, a prerequisite is to represent and communicate the data from the part realisation process in an unambiguous and rigorous manner. In this paper, a semantically enriched LPBF part data model is developed using a category theory-based modelling approach. Firstly, a set of objects and morphisms are created to construct categories for design, process planning, part build, post-processing, and qualification. Twenty functors are then established to communicate these categories. Finally, an application of the developed model is illustrated via the realisation of an LPBF truncheon. [ABSTRACT FROM AUTHOR]

Multi-attribute decision-making (MADM) refers to making preference decisions via assessing a finite number of pre-specified alternatives under multiple and usually conflicting attributes. Many problems in the field of additive manufacturing (AM) are essentially MADM problems or can be converted into MADM problems. Recently, a variety of MADM methods have been applied to solve MADM problems in AM. This generates a series of interesting questions: What is the general trend of this research topic from the perspective of published articles every year? Which journals published the most articles on the research topic? Which articles on the research topic are the most cited? What MADM methods have been applied to the field of AM? What are the main strengths and weaknesses of each MADM method used? Which MADM method is the most used one in this field? What specific problems in AM have been tackled via using MADM methods? What are the main issues in existing MADM methods for AM that need to be addressed in future studies? To approach these questions, a review of MADM methods in AM is presented in this paper. Firstly, an overview of existing MADM methods in AM was carried out based on the perspective of specific MADM methods. A statistical analysis of these methods is then made from the aspects of published journal articles, applied specific methods, and solved AM problems. After that, the main issues in the application of MADM methods to AM are discussed. Finally, the research findings of this review are summarised. [ABSTRACT FROM AUTHOR]

• XCT's capability for dimensional metrology of AM parts is verified by CMS. • XCT beam hardening and XCT voxel size scale error are both investigated. • A new 3D algorithm to estimate uncertainty of the CMS mechanical filtering effect. • Simulation of CMS and XCT are conducted by virtual part with the AM surface texture. This paper aims to verify the capability of X-ray Computed Tomography (XCT) in the dimensional measurement of additive manufacturing (AM) parts by comparing it against a tactile coordinate-measuring system (CMS). Three factors are investigated: XCT beam hardening, XCT voxel size scale error, and CMS mechanical filtering effect. Their performances are compared via both physical and simulation experiments. The physical experiments show that the employed XCT beam hardening elimination aids in obtaining accurate inner dimensions but deteriorates external dimension measurements. XCT voxel size scale error can be compensated by either the two-sphere calibration or the CMS normalisation method. The physical measurement results also indicate that the dimensional deviation between XCT and CMS measurements is mainly due to the mechanical filter effect of CMS, which is further sustained by simulation measurements. The proposed 3D CMS probe scanning simulation method yields a reliable uncertainty estimation of the CMS mechanical filtering effect. [ABSTRACT FROM AUTHOR]