Finite element method (FEM) is significantly helpful to simulate cold spray (CS) deposition for optimizing the process parameters or evaluating the deposit’s physical and mechanical indexes. The simulations’ accuracy, however, is primarily governed by the choice of constitutive material model, while also exhibiting notable sensitive to the mesh size. To enhance predictions and mitigate this sensitivity, in this work, we developed an improved material model able to predict the deformation of the deposited particles with higher accuracy compared to the existing models. The model incorporates strain hardening, strain rate effects, and thermal softening into flow stress, utilizing a straightforward expression that facilitates implementation in FEM via a user-defined VUMAT subroutine and enables efficient experimental calibration. 

The post High-Fidelity Modeling of Cold Spray: Improved Constitutive Material Model and Nonlocal Mesh Sensitivity Mitigation appeared first on Archès Lab.

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.

The post Extending artificial-intelligence-assisted single bead geometry prediction to multi-bead interaction in fused granulate fabrication appeared first on Archès Lab.

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.

In this research, we developed a data-efficient, clustering-aware design framework that combines deep neural networks with genetic algorithms and finite element simulations to generate architected structures with tailored mechanical properties automatically. Our method enables simultaneous control of stiffness, energy absorption, and relative density, moving beyond traditional design strategies. We demonstrate its capability on lightweight, high-performance metamaterials and apply it to optimize orthopaedic implants with biocompatible elastic modulus and enhanced impact energy dissipation. This approach opens new pathways for designing complex heterogeneous materials with targeted performance.

The post Inverse Multi-Objective Design of Three-Dimensional Plate-Based Heterogeneous Mechanical Metamaterials appeared first on Archès Lab.

In response to escalating hygiene concerns, we propose a novel strategy to reduce bacterial adhesion on consumer-grade plastic surfaces (e.g. consumer electronics). Herein, we develop, for the first time in an industrial environment, a process chain using infrared ultrafast laser texturing to create controlled nanotextures that can influence bacterial colonization on plastic surfaces. 

The post A process chain leveraging femtosecond laser induced nanotextures towards mitigating Staphylococcus aureus adhesion on plastic surfaces 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.

Molecular dynamics simulations revealed that cold spray deposition of Cu GNP composites on Al substrates is governed by dual bonding through metallurgical adhesion and graphene metal adsorption. Increasing impact velocity enhances particle flattening and interfacial bonding, with full embedding observed at 1500 m/s. Graphene distribution and particle morphology strongly affect deformation and GNP retention; aggregated GNPs promote interface adsorption, while uniform or random distributions lead to asymmetric deposition. Spherical particles flatten more but form shallower craters than semispherical ones. These findings establish a mechanistic link between velocity, morphology, and GNP configuration, providing guidance for optimizing cold spray of metal graphene nanocomposites.

The post Atomic-level insights into cold spray deposition of Cu-GNPs composite coatings appeared first on Archès Lab.

The post Our Research Featured in the International Thermal Spray and Surface Engineering Magazine appeared first on Archès Lab.

This recent paper from our lab provides a comprehensive view on the current state of the art to facilitate the modulation of cermet deposits’ performance produced using CS technology.

The paper was selected as the Cover Picture of the Journal. This Picture illustrates the dynamics of the impact phenomenon in cold spray deposition, showcasing three different powder configurations: agglomerated, mixed, and clad (from left to right). It depicts various scenarios of bonding, fracturing, and scattering of ceramic particles upon impact, highlighting their distinct behaviors and bonding mechanisms.

The post Current Trends and Future Perspective for Cold Spray Metal-Ceramic Composites 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.