Parametric optimization of fused deposition modelling process using integrated GRA-PCA approach and experimental validation

The present study aims to optimize the process parameters of Fused Deposition Modelling (FDM) through integrated Taguchi approach and Grey Relational Analysis (GRA) associated with Principal Component Analysis (PCA). By combining GRA with PCA, the GRA-PCA approach has leverage the strengths of both techniques to extract relevant features and reduce the complexity of the data, leading to more efficient analysis. During optimization, nozzle diameter, print speed, layer thickness, and width of each layer are considered as an input process parameters and mechanical properties like tensile, compressive, and flexural strength as an output. Further, the outcome of optimization is validated through experimental results. Taguchi L16 array of specific subset of input parameters combinations is utilized to conduct the experimentation. The obtained set of optimized combination of input parameters is found as nozzle diameter 0.3 mm, print speed 60 mm/s, layer thickness 0.2 mm and width of each layer 0.9 mm. Moreover, PCA determined the contribution of tensile strength (43.22%), flexural strength (30.52%) and compressive strength (26.26%) respectively. Confirmatory experimental values for flexural strength 60.7 Mpa, tensile strength 37.7 Mpa and compressive strength 26.1 Mpa respectively which shows 4.71% improvement in WGRG was observed. The GRA-PCA approach offered a synergistic combination of GRA's capability to handle uncertain data and PCA's feature extraction and dimensionality reduction techniques, resulting in improved analysis, interpretation, and decision support compared to individual methods. The outcome of present study may assist to designer and operator of FDM process to achieve high mechanical strength in printed parts via intelligent structure of parameters.