This study introduces a computationally efficient framework that combines an adaptive slicing algorithm and process-specific toolpath planning strategies, designed to optimise deposit accuracy and material efficiency with respect to the model of the part to fabricate. Central to this approach is the integration of predictive models for cold spray deposition, which utilise deep neural networks trained on data from physics-based analytical models.

The framework demonstrates significant improvements in efficiency and accuracy over conventional approaches, paving the way for broader adoption of cold spray additive manufacturing in complex industrial applications.

The post Enhanced geometrical control in cold spray additive manufacturing through deep neural network predictive models appeared first on Archès Lab.

Cold Spray is gaining attention in additive manufacturing for its fast deposition rates, minimal thermal effects, and flexibility in material selection and part size. However, shape control remains a key challenge. The low resolution of spray spots often leads to waviness, tapering, and edge losses, making it harder to achieve precise geometries.

This review takes a closer look at the current efforts to characterize and predict Cold Spray deposit shapes and explores strategies for improving geometrical control, including advanced trajectory planning techniques.

The post Cold Spray Additive Manufacturing: A Review of Shape Control Challenges and Solutions appeared first on Archès Lab.

The post Deposit shape control for local repair and welding by cold spray appeared first on Archès Lab.