We are pleased to share our most recent publication “Enhanced geometrical control in cold spray additive manufacturing through deep neural network predictive models” in Virtual and Physical Prototyping.
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.
