# A Multi-Model Ontological System for Intelligent Assistance in Laser Additive Processes
## 1 Introduction  
Additive manufacturing (AM) technologies for metal parts offer several key advantages, including the ability to produce parts with complex geometries, significantly reduce component weight, accelerate prototype development, and improve material efficiency. In customized and small-scale production, AM techniques serve as a viable alternative to conventional methods of material processing (casting, forging, cutting, etc.) [1]. Their application in production processes opens up the possibility of creating, repairing, and modifying complex structures with improved properties that previously could not be obtained due to technological limitations [2]. The trend of steady growth of the global market of additive technologies is stated today and forecasted in the future by leading consulting companies (Frost & Sullivan, Precedence Research, Zion Market Research, etc.) [3]. Laser-based directed energy deposition (DED-LB) [4] (both powder-based [5] and wire-based [6]), an additive manufacturing process involving simultaneous material deposition and laser energy application, is one of the most technologically complex but promising AM techniques [7]. This technology can be used not only for the fabrication of large-sized metal objects, but also for the restoration of damaged or worn-out parts, as well as for structural modification and functional enhancement of existing parts [8]. Along with the significant advantages of introducing AM technologies, there are also barriers that hinder widespread adoption of AM for metal part fabrication [9]. The main challenge is the need to take into account the influence of numerous laser processing factors and technological parameters on the elemental composition, microstructure of the part material, defect formation (cracks, pores, delaminations, etc., acting on the way to post-processing), as well as the physical and mechanical characteristics of the final product [10]. Currently, the settings for laser processing of metallic materials are often identified by \"trial and error\" method [11]. This approach to the design of AM processes is resource- and time-consuming [12]. Taking into account the interdependence between the process parameters requires interdisciplinary knowledge and professional cooperation of specialists. These requirements contribute to a significant personnel problem, often referred to as the \"high entry threshold\" for laser AM (LAM). Against this background, there is an insufficient level of intellectual support for the process engineers designing AM processes [9; 13]. It is closely associated with the difficulty of scaling technological solutions in the manufacture of metal products by directed energy deposition (DED). There is an acute shortage of long-lived intelligent advisory systems (intelligent assistants) to help designers, technologists and other specialists in determining the proper technological parameters (processing regimes). The fundamental issue lies in the fact that the labor intensity, time and cost of such systems maintenance, which become necessary whenever changes occur in additive manufacturing equipment operating conditions (e.g., range of processed materials) or customer requirements, often make this process unprofitable [14; 15]. Despite significant achievements in the research and development of software systems and platforms [16] to support decision-making in LAM [9; 17], the \"industrial adoption of such systems remains notably limited. Central concepts such as automated knowledge acquisition and inference mechanisms are still in nascent stages, demanding further research efforts\". The latter also applies to \"creating user-friendly software tools that make\" various AI techniques \"accessible to a broader range of AM designers and process engineers\" [13]. These problems determine the relevance of creating a viable intelligent decision support system (DSS) for LAM process engineers [18].