# Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm
## 1 Introduction  
In additive manufacturing (AM), manufacturing processes necessitate the optimal use of raw materials and time factors, as well as physical and operational processes. The performance of the fabrication stages in this manufacturing method can be developed by estimating the optimum printing parameters, which results in less material consumption, a shorter fabrication time, and lower fabrication costs [1, 2]. Machine learning (ML) technology is a verification and prediction method that includes systems that learn from data. The verification and prediction capabilities of this technology can be employed in AM to determine optimal printing parameters that will ultimately provide low costs while improving the quality of the final product. The prediction and optimization of printing parameters using software will help in the development of modern fabrication methods [3, 4]. In parameter optimization, instead of searching for an optimum continuous function, the optimum values of the design variables for a specific problem are obtained [5]. Thus, bydetermining the optimum priming parameters in the AM process, better quality products with higher mechanical properties can be obtained. AM is a manufacturing method that allows for flexibility in design, resource efficiency, and the production of parts made of multiple materials [6]. The advantages of AM include design freedom, rapid prototyping, reduced material waste, and custom manufacturing capability. This method can be particularly effective in the fabrication of parts with a complex geometry, the creation of special designs, or the fabrication of small batches [7]. AM allows for the direct the integration of sensors into the fabrication process, enabling the creation of complex and customized components with embedded sensing capabilities. Sensor-integrated parts fabricated by AM have several applications [8]. In particular, with its AM applications, it can constitute key elements by being applied in the biomedical, electronics, and aviation industries within the framework of Industry 4.0 [9; 10]. Fused deposition modeling (FDM) is a popular method in 3D printing [11], especially for fabricating metal parts with complex geometries [12]. It offers flexibility over traditional machining methods, making it suitable for various industries like drones, medicine, robotics, and defense [13; 14]. The FDM printers use thermoplastic materials melted in a print head, which applies the material layer by layer onto the surface following a specific pattern [15]. Objects can be created from physical ones through 3D scanning and digital modeling, typically exported in an STL format [16]. The model is then converted into code containing print head movements and filament heating parameters to produce the final part by extruding the material at a specific speed and location, initiating the manufacturing process. The FDM processing parameters include wall thickness, build orientation, printing speed, infill density, raster angle, and layer thickness [17; 18]. Depending on the application for which the part is manufactured, the careful selection of these process parameters is required. Despite the simplicity and flexibility of FDM, it has some mechanical limitations, such as material usage depending on the raster angle. The raster angle holds significant importance in the printing of materials, particularly in AM processes, such as FDM. This angle dictates the orientation of successive layers of material as they are deposited during the printing process. The choice of raster angle directly influences various aspects of the final part, including its mechanical properties, surface finish, and dimensional accuracy. Researchers have extensively studied the effects of the raster angle on material printing, recognizing its pivotal role in determining the overall quality and performance of printed parts. For instance, studies by [PERSON] et al. [19] and [PERSON] et al.