Data and code underlying the publication: Rapid Prototyping of a Twin-Screw Granulator for lab-scale research

Twin-screw wet granulation has emerged as a promising solution for continuous manufacturing in the pharmaceutical sector, characterised by its flexibility and ability to produce granules with improved quality and homogeneity. However, challenges related to product formulations and twin-screw geometry affect granule properties, making lab-scale experiments costly. The study underlying this dataset explores the feasibility of using additive manufacturing to develop a functional and transparent twin-screw granulator for lab-scale research. Various printing methods, namely selective laser sintering, fused deposition modelling, and stereolithography, were evaluated for their manufacturing accuracy and surface roughness. An additive manufacturable twin-screw granulator design is proposed based on the Pharma 11 by Thermo Scientific Fisher. The granulator was successfully printed and tested in dry granulation experiments with sesame and canihua, revealing challenges such as gear slip. While the prototype serves as a proof of concept, some optimisation opportunities remain. This dataset contains all CAD files to construct your own twin-screw granulator for lab-scale research. Furthermore, it contains fully working copies of python tools and data (roughness data, measurement data) to plot the figures of the study, verify integrity and make this dataset reusable according to the FAIR principles.

双螺杆湿法制粒（Twin-screw wet granulation）已成为制药领域连续化生产的极具潜力的解决方案，凭借出色的灵活性，可制备出质量与均匀性均得到优化的颗粒。然而，产品配方与双螺杆几何结构相关的诸多挑战会对颗粒性能造成负面影响，同时推高实验室规模实验的成本。本数据集依托的研究探讨了利用增材制造（Additive Manufacturing）研发一款适用于实验室规模研究的功能性、可视化双螺杆制粒机的可行性。研究对三类主流打印工艺——选择性激光烧结（Selective Laser Sintering）、熔融沉积建模（Fused Deposition Modelling）与立体光刻（Stereolithography）——的制造精度与表面粗糙度开展了评估。本研究基于赛默飞世尔科技（Thermo Scientific Fisher）的Pharma 11机型，提出了一款可通过增材制造制备的双螺杆制粒机设计方案。该制粒机已成功打印完成，并采用芝麻与canihua开展了干法制粒实验测试，过程中暴露出齿轮打滑等问题。尽管该原型机已完成概念验证，但仍存在多处可优化的空间。本数据集包含用于搭建实验室规模研究用自主双螺杆制粒机的全部CAD文件。此外，数据集还包含可直接运行的Python工具与各类数据（粗糙度数据、测量数据），可用于绘制研究相关图表、验证数据集完整性，并可依据FAIR原则（FAIR principles）确保数据集具备可复用性。