Several studies explored the significant process parameters largely accountable for enhancing the mechanical properties of FDM printed parts. Gordon et al. [
3] implemented Taguchi orthogonal L
8 array and analysis of variance (ANOVA) to investigate the influence of various process parameters on tensile, fracture strengths, and critical stress intensity factor of FDM printed polylactic acid (PLA) components. They used process parameters such as layer thickness, relative density or infill percentage, extrusion temperature, speed, infill direction, and perimeter layer (or outer shell). Irrespective of orientation, a high value of infill percentage (relative density) is the most influential setting for enhancing the samples' tensile strength and fracture strength. Chacon et al. [
4] analyzed the printing parameters, namely, build orientation, layer thickness, and feed rate, for enhancing the mechanical properties of the FDM parts, such as tensile strength, flexural strength, ductility, and stiffness. It concludes that the upright orientation results in the lowest mechanical properties, while on-edge and flat orientations give the highest strength and stiffness. The mechanical properties increase with increasing layer thickness and decrease with increasing feed rate for the upright orientation. Yao et al. [
5] established a theoretical model based on the transverse isotropic hypothesis, classical lamination theory, and Hill-Tsai anisotropic yield criterion to estimate the ultimate tensile strength of PLA materials printed by FDM in seven different angles (0°, 15°, 30°, 45°, 60°, 75°, 90°) with three-layer thicknesses (0.1 mm, 0.2 mm, 0.3 mm) for each angle. It experimentally observes that tensile strength increases with a decrease in layer thickness from 0.3 mm to 0.1 mm for the same print angle. Sood et al. [
6] studied the effects of layer thickness, orientation, raster angle, raster width, and air gap on improving the mechanical properties such as tensile, flexural, and impact strength of ABS parts printed by FDM. They developed the mathematical model by central composite design (CCD) and analyzed it by ANOVA. They observed that tensile strength increased with increasing the layer thickness, higher value of orientation, increasing the raster angle, and small air gap. A small raster angle increases the stress accumulation and distortion but results in more strength of the built parts. Thick rasters increase the stress accumulation along the width of the part resulting in high temperatures near the boding surfaces, which enhances the diffusion and strong bond formation. Flexural strength decreases with increasing layer thickness at a constant value of raster width. Wu et al. [
7] studied the influence of process variables on mechanical properties like tensile strength, bending strength, and compressive strength of FDM-printed polyether-ether-ketone (PEEK) samples. Three levels of layer thickness and three raster angles were considered process variables of FDM. A layer thickness of 0.3 mm and a raster angle of 0° were observed as optimal process variables to obtain optimal mechanical properties. Lanzotti et al. [
8] developed a second-order response surface model to establish a relationship among process parameters of the FDM process. These process parameters were layer thickness, infill orientation, number of shell perimeters, and the response variables such as tensile strength, elastic modulus, and strain at failure of PLA parts. It observed that ultimate tensile strength (UTS) increases with increasing values of the layer thickness and number of perimeters and decreasing values of the infill orientation. The smaller layer thickness, higher value of shell perimeters, and lower value of infill orientation result in the highest elastic modulus value. Attoye et al. [
9] used the design of experiments (DOE) to optimize different process parameters, i.e., nozzle temperature, printing speed, and print orientation, to ascertain the best values of Young's modulus and yield strength and ultimate strength of FDM manufactured parts. Le et al. [
10] explored the quantitative relationship between process parameters, i.e., layer thickness, deposition velocity, and infill rate, and tensile strength of FDM-built parts made of PLA. The results exhibited that tensile strength and bonding degree decrease continuously with increasing layer thickness values from 0.05 mm to 0.35 mm. The same trend was followed by the tensile strength and bonding degree while increasing the deposition velocity from 30 mm/s to 100 mm/s. These properties continuously increased, increasing the infill rate from 50% to 100%. Liu et al. [
11] proposed an experimental research approach based on the Taguchi method (L
27) and ANOVA to investigate the influence of printing parameters. These parameters were deposition orientation, layer thickness, deposition style, raster width, and raster gap on three evaluation indexes of mechanical properties, such as tensile strength, flexural strength, and impact strength of FDM-built parts made of PLA. Finally, Grey relational analysis searches out a combination of optimal process parameters to optimize comprehensive mechanical properties. It concluded that the deposition rate is the most significant factor, followed by layer thickness and deposition style. Drummer et al. [
12] investigated the influence of different nozzle temperatures to evaluate the tensile strength of FDM printed parts made of biodegradable PLA. Increasing the nozzle temperature produces a high degree of crystallinity. Khatwani et al. [
13] experimentally investigated the influence of process parameters such as nozzle diameter, layer thickness, and part bed temperature on mechanical properties, namely, tensile strength and flexural strength of FDM parts made of PLA. Increasing part bed temperature results in increasing tensile strength and flexural strength. However, tensile strength decreases and flexural strength increases with gradually increasing layer thickness. Akhoundi and Behravesh [
14] extensively investigated the effect of filling pattern and infill percentage on tensile strength, flexural strength, and modulus of FDM printed parts made of PLA. Nugroho et al. [
15] investigated the effect of the different layer thicknesses of PLA parts. The results revealed that higher thickness layers yield the parts of higher strength with lower ductility. Zhou et al. [
16] studied the effect of the printing pattern and infill density on the ultimate tensile strength (UTS)/weight ratio and the modulus of elasticity of FDM printed PLA parts by using Taguchi L9 orthogonal array. The experimental results indicated that smaller air gaps and triangular infill patterns were beneficial for obtaining a good UTS/weight ratio. For the same cross-sectional area, the portion of solid strands to the air gap increases with an increase in infill density resulting in increased tensile strength. Garg et al. [
17] prepared hip replacement implants using an ABS plastic pattern through investment casting. The pattern and implants were optimized to achieve the best surface finish, hardness, and dimensional accuracy by adjusting the process parameters of the FDM process. Taguchi's L18 orthogonal array (OA) was employed to reveal the influence of six process parameters such as type of pattern, V/A ratio of the casting, the orientation of the pattern in the FDM machine, the density of the pattern, mould thickness, and grade of material. ANOVA was applied to find the optimal process parameters for optimizing individual responses. Subsequently, multi-objective optimization results in the implants’ best surface finish, hardness, and dimensional accuracy. Srivastava et al. [
18] performed a multi-objective optimization for responses, namely, built time and support volume in input process parameters such as slice height, contour width, air gap, raster width, raster angle, orientation by using central composite response surface methodology (RSM) design coupled with fuzzy-logic. RSM designs the experiments on the FDM printer with ABS as a filament. The RSM-based fuzzy logic, a multi-objective optimization tool, was successfully implemented for the FDM process. Equbal et al. [
19] performed the multi-objective optimization through the weighted principal component analysis (WPCA)-based desirability function method to obtain the best optimal combination of input process parameters of FDM. These process parameters were layer thickness, air gap, and raster angle for minimizing the relative changes in output characteristics like length, width, and thickness. The RSM-based central composite design performs the experimentation on acrylonitrile butadiene styrene (ABS) polymer. The multi-objective optimization concluded that desired multi-outputs could achieve by the optimal combination of raster width, air gap, and raster angle as 0.4064 mm, − 0.004 mm, and 30°, respectively. Nguyen et al. [
20] performed multi-objective optimization using the Taguchi method coupled with non-dominated sorting genetic algorithm II. The input process parameters, like layer height, infill percentage, printing temperature, printing speed and weight, tensile strength, and printing time as multi-outputs used to develop mathematical models for the FDM process using PLA material.
It is evident from the literature review that various researchers have studied the mechanical properties of the FDM printed parts by considering the influence of a limited number of process parameters. There is thus an enormous scope to thoroughly investigate the mechanical properties of the biopolymer PLA parts printed via the FDM process under the consideration of process parameters as more as possible. Several researchers have performed multi-objective optimization with principal component analysis (PCA), WPCA-based desirability function, RSM-based fuzzy-logic, Taguchi method coupled with non-dominated sorting genetic algorithm II, etc. In the current scenario, Taguchi-based grey-relational analysis has also gained substantial importance because of transforming multi-responses into a single function that is, of course, easy to handle. In actual practice, the fabricated part must possess more than one mechanical property concurrently to overcome various loadings. Therefore, it is necessary to trade off various mechanical properties in a fabricated part per the customers' demand. Grey relational analysis has been employed for multi-response optimization of quality characteristics to address this concern.