Incorporation of machine learning in the design of bio-nanocomposites produced by DLP 3D printer

Abstract

Due to its ease of processing and compatibility with biological systems, polylactic acid (PLA) sees extensive use in biomedical applications. This research explores the mechanical characteristics and wear resistance of PLA biocomposites fabricated via Digital Light Processing (DLP). To create both single-component and hybrid nanocomposites, alumina (Al2O3), silica (SiO2), and graphene (GS), all biocompatible materials commonly found in medical devices, were added to the PLA matrix. The study evaluated how printing layer thickness, the type, and the amount of reinforcements affected the final product's properties. The collected data were analyzed utilizing machine learning methodologies to predict bio-nanocomposites' characteristics. The results indicated that the thinner printing layer exhibits higher strength and wear resistance. The tensile strength improved by similar to 16 % while raising the alumina content to 8 wt.%. However, silica and graphene deteriorate the tensile properties. Concerning the wear properties, even though all reinforcements increase the wear resistance, it is enabled more severely by graphene with 77 % improvement, while silica (with 69 %) and alumina (with 62 % improvement in wear resistance) stand on the second and last level. In hybrid nanocomposites, the silica and graphene reinforcements reduce the strength, despite improving the wear rate