Basket granulation is an extrusion technique extensively used in pharmaceutical and food industries due to its ability to produce high-quality extrudates under low extrusion pressures, minimising thermal buildup and preserving formulation integrity. Despite its industrial significance, understanding of how formulation properties and process parameters influence extrusion dynamics and product quality remains limited. This thesis aims to systematically investigate the effects of formulation characteristics (e.g. cohesiveness, consistency, compressibility, and flowability) and process conditions (rotor speed, screen size, die thickness, roller radius, and drying time) on granule quality (shape, size, and strength) through experimental studies, process modelling, and machine learning.

This thesis also addresses the absence of characterisation metrics for extrusion performance, limited information into extrusion dynamics and forces, and the lack of a regime map defining optimal operational windows. Additionally, it tackles the underutilisation of modeling techniques such as Discrete Element Method (DEM) simulations and machine learning tools in investigating basket granulation.

This thesis introduces a new Extrudability Factor to quantify extrudability and correlates it with extrudate strength and sphericity. In addition, a regime map is developed to define the optimum extrusion window as a function of Extrudability Factor and Normalised Screen Pressure. Moreover, using an instrumented rotary extruder with customizable dies, the relationship between extrusion force and the Composite Solidity Aspect Ratio (CSAR) is identified. A physics-informed neural network (PINN) is also developed, enabling predictions of extrusion force and optimal process conditions with limited experimental data. Additionally, a novel linear extrusion setup is developed, and corresponding DEM simulations are performed, which was used to examine the effects of surface tension, viscosity, and packing density on flow behaviour and granule morphology. Asymmetric trajectories, velocity gradients, and localised densification are observed. The modified Capillary Number ( ) is used to describe the liquid saturation level of the wet formulation. Optimal extrusion occurs within a  range of 10⁻⁴ to 10⁻², producing dense, cohesive extrudates with minimal defects.

The integration of novel metrics, regime maps, and advanced modeling tools provides a foundation for predicting and optimising extrusion performance. The knowledge gained not only contributes to a mechanistic understanding of the process but also paves the way for improved process design and consistent product quality.