## 1 Introduction  
Powder bed fusion (PBF) is established additive manufacturing group of processes, which include, among others, laser powder bed fusion (LPBF), electron beam melting (EBM) and multi-jet fusion (MJF). This layer-by-layer fabrication process involves the selective melting of a powder bed, enabling the creation of components with geometries that are difficult or impossible to achieve with traditional manufacturing techniques. The technology has become increasingly adopted by the end user. Although the acquisition cost of a professional 3D printer is still high, the adoption is facilitated by the emergence of outsourcing services, which provide remote professional printing, advanced materials, and expertise for clients needing high-quality parts [1]. The applications of 3D printing have been documented across industries ranging from gas turbines engines parts [2] to medical implants [3] and copies of cultural heritage object [4]. This technology has advanced even further with 4D printing, which introduces time as a functional element, allowing printed objects to change shape or properties in response to stimuli like heat or moisture [5]. Laser powder bed fusion (LPBF) is affected by many parameters, including laser parameters, the scanning strategy, the powder spreading method, and support design, and still needs optimalization [6]. In this article, the focus is laid on powder spreading. The quality and integrity of the final product are highly dependent on the quality of the powder layer, which is described by its uniformity and packing density. The laser powder thickness and the laser scanning parameters are correlated through the volumetric energy density, as defined by Equation (1):\[E_{\text{p}}=\frac{P}{v\text{-}d\text{-}t} \tag{1}\]  
where \(P\) stands for output laser power (W), \(v\) stands for scan speed (mm/s), \(d\) stands for scan spacing (um), and \(t\) stands for layer height of the powder (um) [7]. During the process, the powder layer may be disturbed due to, for example, recoater damage, changes in powder morphology, or the unevenness of the molten layer [8]. These disturbances can alter the recoater gap, causing the powder spreading process to either undercoat or overcoat. Improper powder spreading can lead to variations in volumetric energy density, causing incomplete melting or inadequate fusion between powder particles, which increases the porosity levels in the final part [9]. Therefore, an optimal powder layer should have consistent and uniform packing density and a relatively smooth surface finish, minimizing the discrepancies between the actual and theoretical layer thicknesses [10]. Numerous studies have been conducted to optimize powder spreading techniques, both numerical and experimental [11; 12]. The investigations concerned, among others, the shape of the recoater [13], the powder size distribution [14], or the blade velocity and height [15]. Recently, machine vision inspections are applied to effectively monitor the LPBF forming process as a safe, reliable, and highly automated non-contact technology. Integrating vision systems with dedicated algorithms facilitates in situ detection of various defects in the powder bed, including recoater hopping, recoater striking, incomplete spreading, and super-elevation [16]. The analysis of these defects can provide detailed insights into the origin of both small and large flaws, enabling a comprehensive analysis of defect distribution throughout the process and can be used to optimize process parameters [17]. [PERSON] et al. [18] have shown that powder bed anomalies directly correlate with the likelihood of defect formation. In addition, as the severity of the powder bed errors increases (indicated by the larger size of the error), the probability of defect formation also significantly rises. The simplest solution to identify powder defects like blade wear was proposed by [PERSON] et al. [19].