Insights into drop-on-demand metal additive manufacturing through an integrated experimental and computational study

Gilani, N.; Aboulkhair, N. T.; Simonelli, M.; East, M.; Ashcroft, I.; Hague, R. J.M.

Abstract

Drop-on-demand metal jetting is a recent additive manufacturing technology opening new opportunities for the fabrication of complex single and multi-metal components. MetalJet, the Océ developed technique used in this study, has the capacity to produce molten micro-droplets (60–80 µm) at temperatures up to 2000 °C to form single and multi-material objects. Applications for this technology include flexible circuits, advanced electronic components and biotechnologies. However, full exploitation of this technology is impeded by a lack of understanding of various aspects of the process, including droplet bonding and interface formation, residual stress development and the evolution of microstructure. This paper uses an integrated numerical and experimental approach to provide insights into these research questions. Thermal models were used to investigate droplet-to-substrate adhesion and explain the experimentally-observed morphology of droplets. Thermo-mechanical modelling was used to investigate residual stress development and its role in the observed droplet warping and delamination. The knowledge obtained from this study can be used to underpin the development of functional multi-material printing.

Citation

Gilani, N., Aboulkhair, N. T., Simonelli, M., East, M., Ashcroft, I., & Hague, R. J. (2021). Insights into drop-on-demand metal additive manufacturing through an integrated experimental and computational study. Additive Manufacturing, 48(Part B), Article 102402. https://doi.org/10.1016/j.addma.2021.102402

• Journal Article Type: Article
• Acceptance Date: Oct 9, 2021
• Online Publication Date: Oct 15, 2021
• Publication Date: Dec 1, 2021
• Deposit Date: Oct 21, 2021
• Publicly Available Date: Nov 23, 2021
• Journal: Additive Manufacturing
• Print ISSN: 2214-8604
• Electronic ISSN: 2214-8604
• Publisher: Elsevier BV
• Peer Reviewed: Peer Reviewed
• Volume: 48
• Issue: Part B
• Article Number: 102402
• DOI: https://doi.org/10.1016/j.addma.2021.102402
• Public URL: https://nottingham-repository.worktribe.com/output/6506207
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S2214860421005558