Crystallinity development in material extrusion additive manufacturing of semicrystalline polymers: A review

The use of semicrystalline polymers in Material extrusion additive manufacturing (MEAM) remains challenging due to the complex interplay among thermal history, crystallisation kinetics and flow dynamics. The rapid, non-isothermal conditions inherent to the process govern nucleation, crystal growth, and molecular orientation, ultimately determining the microstructure and the resulting mechanical, thermal, and structural properties of printed parts. This review has provided the current state of the art on the analysis of crystallinity development in MEAM of semicrystalline polymers. Fundamental aspects of quiescent and flow-induced nucleation have been discussed alongside modelling strategies that couple heat transfer with crystallisation kinetics. Modelling studies have been reviewed to understand the current accuracy of simulation tools in the prediction of temperature profile, crystallinity degree and dimensions of spherulitic structures. The role of process parameters in determining the mechanical performance has been critically analysed. Particular attention has been given to the competition between crystallisation and interfacial diffusion, which governs interlayer bonding and mechanical performance. The analysis has revealed that process parameters impact on the cooling rate, the polymer chain orientation, part density and porosity beyond the crystallinity degree. Emerging methods for monitoring crystallisation phenomena during the MEAM process have been reviewed, highlighting that X-ray diffraction remains the most precise. Strategies for controlling crystallinity, including process optimisation and thermal post-treatments, have also been examined in relation to their role in improving mechanical performance. Morphological features typical of MEAM-printed semicrystalline polymers have been collected, such as anisotropic structures due to flow-induced crystallisation on the filament surface and larger spherulites in the inner region under quasi-quiescent conditions. Finally, emerging machine learning methodologies have been discussed as promising tools for capturing the complex, multivariate nature of MEAM and enabling predictive materials design.