A convolution-based approach to cold spray additive manufacturing

Venturi, F.; Gilfillan, N.; Hussain, T.

Abstract

Cold Spray Additive Manufacturing (CSAM) is a well-established technology that has recently attracted interest for forming 3D shapes in a fast and scalable fashion. Nonetheless, the resulting surface of cold sprayed parts normally requires post-deposition machining to achieve the desired surface finish. In this work, a convolution-based digital framework for CSAM yield and surface finish prediction able to calculate the optimal interline distance to reduce surface waviness was developed. The aim is to minimise post-deposition treatments, thereby reducing production time, material waste and costs. This method is applicable beyond CSAM and can be of interest for other additive manufacturing techniques.

Citation

Venturi, F., Gilfillan, N., & Hussain, T. (2021). A convolution-based approach to cold spray additive manufacturing. Additive Manufacturing, 1, Article 100014. https://doi.org/10.1016/j.addlet.2021.100014

• Journal Article Type: Article
• Acceptance Date: Oct 19, 2021
• Online Publication Date: Oct 29, 2021
• Publication Date: 2021-12
• Deposit Date: Apr 4, 2023
• Publicly Available Date: Apr 18, 2023
• Journal: Additive Manufacturing Letters
• Print ISSN: 2214-7810
• Electronic ISSN: 2214-8604
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 1
• Article Number: 100014
• DOI: https://doi.org/10.1016/j.addlet.2021.100014
• Keywords: CSAM; Low pressure cold spray; Convolution; Digitalisation; Predictive manufacturing
• Public URL: https://nottingham-repository.worktribe.com/output/19259512
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S2772369021000141?via%3Dihub
• Additional Information: This article is maintained by: Elsevier; Article Title: A convolution-based approach to cold spray additive manufacturing; Journal Title: Additive Manufacturing Letters; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.addlet.2021.100014; Content Type: article; Copyright: © 2021 The Authors. Published by Elsevier B.V.