Breakthrough to the pragmatic evolution of direct ink writing: progression, challenges, and future

The potentiality to construct 3D geometries by harnessing multifaceted materials for both conventional and unprecedented methodologies has become a paramount component of the additive manufacturing industry. The direct ink writing (DIW) process, also entitled Robocasting, is appertaining to the conventional casting process and facilitates the fabrication of complex architecture made up of peculiar materials, which are to be transformed into ink that procures shear-thinning and viscoelastic properties to be printed in patterns. Several types of research have been carried out on fabricating a variety of components with multifarious materials by employing direct ink writing, but very few articles are present to date pertaining to DIW process parameters and their influence on ink rheology. This review paper is devoted to explicating the DIW technique, appertaining to aggrandized emphasis on preponderate process parameters like nozzle diameter, extrusion rate, nozzle substrate distance, extrusion pressure, layer thickness, and their influence on ink rheology, ink flow, and also broad versatility of the techniques used for printing variety of materials. The delineation of the utilization of newly emerging technologies like machine learning for atomizing the control of process parameters and designing ink rheology models to incipient the accuracy and resolution of DIW is specified. Finally, the future potential of DIW is detailed and elucidated to address future challenges in bio-medical, electronics, and soft robotics applications.

Automated summary

This paper focuses on the significance of the direct ink writing (DIW) process in the additive manufacturing industry, which enables the construction of 3D geometries using multifaceted materials. It emphasizes the influence of key process parameters, such as nozzle diameter, extrusion rate, nozzle substrate distance, extrusion pressure, and layer thickness, on ink rheology and flow. The paper also mentions the application of emerging technologies, such as machine learning, for controlling process parameters and improving ink rheology models.