Multi-Parameter Optimization, Machine Learning-Based Prediction, and Surface Characterization of Bio-Based FDM Composites

Abstract

In this study, mechanical and morphological behavior of the bio-based composites produced by fused deposition modeling are experimentally and data driven investigated. Three distinct material configurations were fabricated and examined with the help of a Taguchi L27 orthogonal array which included pure PLA, PLA/Wood composite and multilayer sandwich structures. Five important process parameters were optimized viz. material type (MT), infill density (ID), printing speed, infill pattern and nozzle temperature. Tensile and compression tests were performed according to ASTM D638 and ASTM D695 standards. The PLA samples showed the highest tensile strength (48.66 MPa). The compressive strength was also highest for the PLA samples (45.69 MPa). ANOVA results showed that MT was the most influential factor on tensile strength and ID was the strongest effect on compressive strength. Scanning electron microscopy observations showed differences in interlayer adhesion, porosity, and fracture behavior of the material systems. Furthermore, the Taguchi based prediction models, decision tree regression and Gaussian process regression (GPR) were also developed. Among these models, GPR showed the best predictive performance with R 2 values above 0.995 and MAPE values below 14%, exhibiting good agreement with the experimental results.