## 1 Introduction  
The ability to manufacture intricate geometries [1], the high levels of customization, and other benefits have contributed to the growth of additive manufacturing (AM) technology over the past 25 years [2, 3]. One of these benefits is its reduced environmental impact due to the process's reduced material waste [4, 5, 6]. To understand this facet of the AM technique and its role in a cyclical economy, as well as the equivalent environmental perceptions, a lot of research has been conducted [2, 4, 5, 7, 8]. Among the several methods of AM, Fused Filament Fabrication (FFF) is a material extrusion (MEX) process involving layer-by-layer [9] melting of filament strands through a heated nozzle to create objects using Computer-Aided Design (CAD) data [6, 10, 11]. The impact of the processing variables, such as the infill pattern, on the mechanical performance and dimensional precision of items fabricated using the FFF technique has been investigated [12]. The energy used throughout the 3D printing of components is one of the research topics that have an impact on the sustainability of the AM method [2; 13]. To establish the ecological effect of AM [14; 15], researchers have examined the consumption of energy of the AM procedure [16; 17]. It is reported that in the vat photopolymerization (VPP) process, the energy consumption varies from (Specific Energy Consumption--SEC) 21 to 33 KWh/Kg, while in the FFF process, it ranges from 23 to 346 KWh/Kg for the 3D printing of the ABS polymer [14; 15]. From this energy, 51.7% is the energy consumed by the motors, 41.4% by the heat elements, and 6.9% by the fans [16], while a model for the prediction of the energy consumption has been proposed [17]. Polymers such as acrylonitrile butadiene styrene (ABS) [18], polylactic acid (PLA) [19; 20], poly (methyl methacrylate) (PMMA) [21], and polycarbonate (PC) [22], among others, have been investigated for their energy demands during the 3D printing of parts with the MEX method. Adaptive multi-layer customization [23], machine learning methods [24], and statistical modeling tools [25], made for examining and maximizing the influence of the 3D printing settings on the consumption of energy in methods of additive manufacturing [26], have all been used in this context. Modeling tools, such as Neural Networks [27], Analysis of Variances (ANOVA) [28], Taguchi design of experiments [29; 30], Box Behnken design [31], have been applied for the analysis of experimental data in 3D printing, related to the effect of the 3D printing settings on the performance of the parts. Additionally, the economic viability and ecological impact of FFF, both have an extensive amount of room for improvement [32]. Numerous studies have been conducted to determine how the 3D printing factors influence the mechanical properties of the final component [26; 33; 34]. A crucial step in creating 3D geometries is choosing the appropriate infill pattern as well as the printing speed [12; 35]. [PERSON] et al. [36] examined the impact of printing speed and layer height on composites consisting of ABS with hydrous magnesium silicate using the FFF process. By properly adjusting the process parameters, the mechanical performance of the fabricated objects can be greatly enhanced [37]. The optimization of process parameters has drawn significant interest from numerous researchers. Some of these parameters include the speed of printing [34], the diameter of the nozzle, the raster angle [38], layer thickness [39], and chamber temperature [40], which are often optimized using the Taguchi method [41]. [PERSON] et al. [42] investigated how the mechanical properties of 3D-printed poly-lactic acid (PLA, a thermoplastic material) pieces were affected by the orientation angle of the parts [43]. A constitutive model for 3D printed parts was developed by [PERSON] et al. [44], and this model can depict how layer thickness and building orientation affect the way 3D printed items respond based on their materials.