## 1 Introduction  
In engineering applications, additive manufacturing (AM) methods are characterized by a number of advantages that determine the suitability of their use in unit production and prototype production [1; 2; 3; 4; 5]. First of all, they ensure ease of production preparation--it is not necessary to construct special equipment, molds, dies, or fixtures. There is also no need to develop the sequence of operations and settings because the entire process takes place in one operation and setting [1; 4]. The FFF (fused filament fabrication) method is universal, and a thermoplastic polymer is used as the model material [5; 6]. The construction of a semi-finished product is also unnecessary, because, in the vast majority of cases, the item is ready-made, without the need for subsequent processing [7].  
In the available works, there is a lot of research on process parameter optimization, which potentially allows for obtaining increased dimensional accuracy [8; 9; 10; 11; 12]; however, such works are strictly related to the process parameters but do not take into account all factors that are connected to the topology of the produced part. Some other approach has been suggested by [PERSON] et al. [13], who developed a tool that allowed for a kind of calibration of the part that is going to be manufactured, based on experimental data. Such a tool could be used to minimize geometric inaccuracies, such as thickness errors and printing time. [PERSON] et al. [14], in their work, proved that using small layers during AM production leads to a better definition of the geometry of the features, but in some cases using higher layer values (0.21 mm versus 0.10 mm) allowed for obtaining better ISO IT grades. To overcome such technological obstacles, [PERSON] et al. [15] developed a tool for obtaining better dimensional quality based on the voxel volumetric representation. The main limitation of this proposed method is related to the vastness of the information associated with this representation scheme. A new approach is becoming more available with the development of artificial intelligence and statistically-based software (AI) [16; 17]. Such tools make it possible to predict the geometrical dimensions with their deviations, including process parameters and other factors that are not directly related to the AM process.  
The purpose of the accuracy and repeatability of the research was to analyze the dimensionality and shape of the FFF technology, carried out on the example of the developed pattern. Analyses allowed a more complete understanding of the limitations of additive technologies and also helped to determine the probability that a defective detail does not meet the conditions of geometric accuracy [18; 19; 20; 21; 22; 23; 24; 25], which leads to a justification of the use of statistically based and AI-supported tools to predict part dimensions and deviations. Unlike previous studies that often focus solely on process parameters, this research considers the complete topology of the produced part and incorporates a broader range of factors, thus providing a more holistic understanding of the limitations and capabilities of additive technologies.