## 1 Introduction  
In additive manufacturing (AM), a three-dimensional geometry is decomposed in bi-dimensional shapes and used to create solid parts that are built layer upon layer [1]. This basic workflow is common to different manufacturing processes, like material jetting (MT), material extrusion (MEX), and powder bed fusion (PBF) [2]. The application of AM has evolved from prototypes to small-batches, but the objective of reaching mass production capabilities would require filling the gap between AM specification standards and the industrial needs [3]. In fact, the achievable quality is still affecting the adoption rate of AM in the consumer market [4]. Tolerance intervals (TI) are expected to be larger in AM than in traditional manufacturing processes [5], and this lack of quality has been usually addressed by adding post-processing steps [6]. Production planning in AM will require trustable information about the expected TI and the fulfillment of tolerance specifications to decide which machine or technology will be used beforehand [7]. Those works investigating dimensional quality in the AM process are frequently focused on providing a quantification of dimensional quality [8; 9; 10; 11; 12], analyzing the influence of different factors upon dimensional accuracy [13; 14; 15; 16; 17], or proposing strategies to improve -dimensional quality [18; 19; 20; 21]. [PERSON] [13] modelled the dimensional deviation of parts manufactured in MEX machines as a function of the deposition angle and layer thickness. The model was verified for different materials and angles before being applied to a multi-feature case study. [PERSON] [18] used the deviations between the manufactured surface and the target one to compensate shrinkage in a stereolithography process. [PERSON] [14] used cuboids to analyze the achievable tolerances in a MEX machine, considering orientations along \(X\), \(Y\) and \(Z\) axes. Parts were manufactured in several positions, but the results were averaged and the possible influence of position upon results was not considered. Measured dimensions were found to be dependent on orientation and nominal size, and the authors provided an estimation of the correspondent TI amplitude and location. [PERSON] proposed a reference multi-feature part that was used to evaluate the dimensional accuracy of AM systems [8]. This researcher has also used the proposed test specimen to perform benchmarking comparisons between different MEX machines [8; 9] and between different AM processes [12]. In these works, the deviation of each measured feature from its nominal size was divided by the tolerance factor \(i\) that corresponds to a range of basic sizes according to ISO 286-1:1988 [22]. The results were later used to determine the maximum dimensional error expected for a specific feature size range, fitting it into a particular TI. [PERSON] [15] proposed a series of benchmark artifacts to investigate process capability and applied them to MJT. They analyzed the influence of process parameters upon dimensional quality, seeking for an optimal process configuration. [PERSON] [10] used a test artifact derived from the one proposed by NIST [23] to evaluate the capabilities of a MEX equipment as part of their effort to develop a smart manufacturability assistant. [PERSON] [17] proposed a test artifact and an experimental strategy to evaluate dimensional and geometrical quality in PBF. This works paid attention to the differences in dimensional quality related to position and orientation of the part within the working space, but also to the variation related to consecutively manufactured trays. Their conclusions pointed to a negligible variation of measurements between builds, whereas a clear influence of the \(x\)-\(y\) position upon part dimensions was observed. Benchmarking artefacts frequently use a multi-feature approach to provide a global perspective of the achievable quality in terms of machine or process comparison.