## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Area of Knowledge about AM Technologies** & **Methods/Tools** & **Results** & **References** \\ \hline Product design & Formalising AM knowledge based on descriptive logics (DLs), Design for Additive Manufacturing (DfAM). & Obtaining and analyzing data on the mutual influence of geometric design and process parameters in order to meet the requirements of AM technology specificity. & [11] \\ \hline Implementation of the best technology & Method to self-evaluate the potential of AM, accompanied by a method to validate the technical feasibility of AM—AHP (Analytic Hierarchy Process). & Stage analysis and selection of the right AM process for the needs of the company. & [12] \\ \hline Manufacturing planning process with various AM systems & The IDEF0 diagrams, Pareto chart, Ishikawa diagram. & Process planning and defining the sequence of tasks and the messages flow. & [13,14] \\ \hline Quality and throughput of AM processes. & DfAM, AHP, Multi-Attribute Decision Making, Multiple Objective Optimization (MOO), Integrated Product and Process Development (IPPD) method, Formal Concept Analysis (FCA). & Data analysis to use the integrated process chain, from component design to pre-treatment, production (construction of the laser unit), finishing and finished parts. & [15] \\ \hline The identification of effect mechanisms and key factors in the production process & The method identifies key parameters and impacts and finds new expected correlations using similar and related technologies. & Design a basic theoretical approach for a process model of additive manufacturing with metals, analysis a changes taking place when moving from a solid phase to a liquid phase or back again., analysis a interaction between the laser beam and the powder material. & [16] \\ \hline Management of data that is generated in the AM value chain. & Ontology-based modeling approach: Basic Formal Ontology (BFO), Information Artifact Ontology (IAO), Common Core Ontology (CCO). & Backbone structure to organize AM data and automatically reason over experts’ knowledge for data validation, ultimately supporting the development of algorithms and applications for decision making. & [17] \\ \hline Prediction of selective laser melting part quality & Designs of experiments (DOEs), model that relates process parameters to part quality—hybrid Bayesian, another Bayesian network. & analysis of the relationship between AM process parameters and various parts quality characteristics & [18] \\ \hline Final quality of produced parts & Digital models, numerical modeling. & analysis of operational parameters which influence the production process in additive manufacturing and quality of produced components by AM technology. & [19] \\ \hline Smart manufacturing & Knowledge-driven framework for digital twin manufacturing cell (DTMC). & Data analysis and support autonomous manufacturing by an intelligent perceiving, simulating, understanding, predicting, optimizing and controlling strategy. & [20] \\ \hline Materials & The MKS (Materials Knowledge Systems framework. & Gathering and analysis of knowledge about materials: mechanical properties, microhardness, material structure, etc. & [21] \\ \hline Area of demand and application of AM technology & Bayesian networks & Gathering of knowledge about demand and application of AM technology within a manufacturing company, support a managers to take the decision to implement AM technologies. & [20] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Defining areas of knowledge about AM technologies in manufacturing companies. demand for AM area in manufacturing enterprises as a result of combining the results of the literature research about the state of the use AM technologies in manufacturing companies and knowledge obtained through empirical research.