Prediction of curing depth dependence on CNT nanofiller dispersion for vat photopolymerization 3D printing

Abstract

Carbon nanotubes (CNTs) have been often implemented as an additive in photopolymer resins for photo-reactive manufacturing processes such as vat photopolymerization (VP) 3D printing to endow various functionalities and improve properties of components. However, CNTs are known for their effective UV absorption and strong agglomeration tendency, which reduce the 3D-printability of the resin composites. Moreover, since varying dispersion qualities and concentrations of CNTs significantly influence curing depth photo-curing characteristics, it remains a challenge to determine the appropriate curing parameters. Consequently, many time-consuming and tedious experiments are required to find the proper curing parameters for adequate manufacturing. To overcome the need for extensive experimentation, a theoretical model that considers both the dispersion and concentration of CNTs needs to be established to provide appropriate curing parameters. This work introduces a simple model applying a modified Beer-Lambert's law that integrates both the degree of dispersion and concentration of CNTs, aiming to predict curing depth and subsequent printability of CNT reinforced photo-curable resins. Through this model, the curing depth can be predicted depending on the degree of dispersion and concentration via only two spectroscopic experiments and one curing experiment, thus significantly reducing time-consuming and material extensive experimental works for VP 3D printed CNT/resin composites. Results show that the curing depth decreases with improved dispersion and increasing concentration of CNTs as a result of higher degrees of light path obstruction. More significantly, the methodology used to construct our model is also applicable to the development of other photo-curable resin composites used in various stereolithographic manufacturing methods.