Additive manufacturing (AM) technology has gained significant popularity, among which Fused Deposition Modeling (FDM) has emerged as the predominant technique for 3D printing. FDM offers the unique ability to achieve the desired and tailored engineering properties required for specific applications. This experimental study investigates the influence of varying FDM process parameters on the mechanical properties and highlights the optimal set of parameters for better tensile strength for a bi-layered composite of PLA-ABS (polylactic acid and acrylonitrile butadiene styrene). Also, it investigates the most-to-least influential printing parameters. Four process parameters were played out i.e., Infill density (50%, 75%, and 100%), number of layers (20,25 and 30), printing speed (20 mm sec⁻¹, 40 mm sec⁻¹, and 60 mm sec⁻¹), and bed temperature (90 °C, 95 °C, and 100 °C), while keeping other parameters constant. Taguchi optimization technique was used for optimization and experiments were designed according to Taguchi orthogonal array L9 (3 4). After printing, the samples were evaluated for tensile properties and the results were analyzed. It is found that the infill density is the most influential parameter while bed temperature is the least influential. Infill density of 75%, 30 layers per part, a printing speed of 20 mm sec⁻¹, and a bed temperature of 100 °C are the optimal set of parameters for better tensile strength. Alongside, percent elongation, printing time, and strength-to-weight ratio were also analyzed and correlated.

Efficiently managing multiple process parameters is critical for achieving optimal performance in additive manufacturing. This study investigates the relationship between eight key parameters in fused deposition modeling (FDM) and their impact on responses like average surface roughness (Ra), tensile strength (TS), and flexural strength (FS) of carbon fiber-reinforced polyamide 12 (PA 12-CF) material. The study integrates response surface methodology (RSM), grey relational analysis (GRA), and grey wolf optimization (GWO) to achieve this goal. A total of 51 experiments were planned using a definitive screening design (DSD) based on response RSM. The printing process parameters, including layer thickness, infill density, and build orientation, significantly affect Ra, TS, and FS. GRA combines responses into a single measure, grey relational grade (GRG), and a regression model is developed. GWO is then employed to optimize GRG across parameters. Comparison with GRA-optimized parameters demonstrates GWO’s ability to discover refined solutions, reducing average surface roughness to 4.63 μm and increasing tensile strength and flexural strength to 88.5 MPa and 103.12 MPa, respectively. Practical implications highlight the significance of GWO in industrial settings, where optimized parameters lead to reduced costs and improved product quality. This integrated approach offers a systematic methodology for optimizing FDM processes, ensuring robustness and efficiency in additive manufacturing applications.