Cold Spray Additive Manufacturing: A Review of Shape Control Challenges and Solutions

Abstract

Cold spray (CS) is a promising solid-state deposition method that offers several advantages over traditional thermal spray techniques. With rapid deposition, minimal thermal degradation and distortion, and unique flexibility in material selection and part size, it is an attractive option for additive manufacturing. Despite the latest steep technological advancements, a significant hindrance to the wide application of CS in this field is shape accuracy. The Gaussian-like deposit profiles characteristic of CS limit its resolution, causing waviness along the deposit, tapering, and edge losses, making shape control a difficult task. Deposit shape modeling can play a major role in addressing this challenge and counterbalancing the restrictive resolution issues by predicting the deposit shape, as a function of kinetic process parameters. Macroscale deposition modeling can furthermore boost automated process planning for high geometrical control. This paper depicts the current scenario and ongoing attempts to characterize and predict CS deposit shape. It categorizes CS shape prediction models into Gaussian-fit, physics-based, and data-driven. Through the critical evaluation of such models, research gaps and potential areas of improvement are identified, particularly in simultaneously achieving high prediction accuracy and computational efficiency, rather than framing them as competing objectives. Alternative recently developed strategies for geometrical control are furthermore explored, including advanced trajectory planning techniques, tailored to CS.