Accurate prediction of the cross-sectional geometry of deposited beads is essential for improving process control in Fused Granulate Fabrication (FGF), a key process within the Large Format Additive Manufacturing (LFAM) family. Building upon the previous model for single bed shape prediction, this work addresses the complex problem of reconstructing the full cross-sectional shape of polymer beads in multi-bead configurations, focusing on both adjacent and superimposed beads, through an Artificial Neural Network (ANN). A structured dataset was generated by varying critical process parameters, namely layer height, screw speed, and bead center distance. The ANN, designed with two hidden layers and supported by image processing techniques, successfully captured the geometric features of the deposited material, reaching a mean absolute error of 10.22% across all tested conditions. Unlike traditional methods that approximate only a limited number of contour points, the approach proposed here, enables full-profile prediction, offering a deeper understanding of bead interactions and the dynamics of layer formation. The findings represent a significant step forward aimed at improving the geometric accuracy and the process control in LFAM applications, contributing to a better understanding of the role of the key process parameters.

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In this study, a computational fluid dynamic (CFD) model was developed to simulate the cold spraying process. The simulations were repeated on a wide range of process parameters and on different substrate geometries, and the generated data was used to train an artificial intelligence (AI) model of Support Vector Regression (SVR) with the objective of directly predicting the thermo-kinetic properties of the metallic powders. To strengthen the interpretability of the prediction model, the explainable AI method of SHapley Additive exPlanations (SHAP) was implemented to identify how each input parameter affects the model predictions for particle temperatures and velocities. The combined CFD-AI approach showed high accuracy and efficiency in predicting the thermo-kinetic conditions of the powder while maintaining the physical interpretability of the related phenomena. This integrated method enables advanced optimization strategies for controlling the Cold Spray process.

The post Integrating computational fluid dynamics and artificial intelligence for predicting in-flight thermo-kinetic properties in cold spray appeared first on Archès Lab.

The purpose of this study is to provide a comprehensive review on the technologies of Industry 4.0 already integrated with thermal spray processes to uplift their applications through advanced digitization and automation, leading to enhanced efficiency and sustainability.

The post Outlook of Industry 4.0 Integrated Technologies in Thermal Spray Processes and Applications appeared first on Archès Lab.

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.

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