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.

Our group at Archés Lab just had the exciting opportunity to participate to the International Thermal Spray Conference (ITSC26) in Bangkok, Thailand. 
This is a unique annual appointment to come in direct contact with the major exponents and latest innovations within the thermal spray community. We had the pleasure to contribute by sharing our research on Cold Spray through five oral presentations:

• Prof. Sara Bagherifard: “The Role of Powder Particle Mechanics in Advancing Cold Spray Technology”
• Prof. Mario Guagliano: “Investigating Cold Spray Repairs in Fretting-Affected Al-7Si-Mg Alloy”
• Dr. Amir Ardeshiri Lordejani: “A Finite Element Approach for Multi-Faceted simulation of Ceramic-reinforced Metal Composite Cold Spray Deposition”
• Roberta Falco: “A Hybrid Machine Learning Framework for Predicting and Optimizing Cold Spray Defect Repair”
• Fatemeh Zarei: “Impact of Powder Properties on Microstructural and Mechanical Anisotropy in Cold Spray Additive Manufactured Copper”

 

The post International Thermal Spray Conference (ITSC26) in Bangkok, Thailand appeared first on Archès Lab.

Prof. Bagherifard’s research in solid-state deposition, particularly cold spray coatings and hybrid additive manufacturing, advances structural and functional coatings for critical sectors such as aerospace, materials engineering, and high-performance manufacturing. Her work spans the development of hierarchical, heterogeneous materials (ArcHIDep), exploration of high-entropy alloys, metal-ceramic composites, and polymer substrates, as well as innovative post-processing methods like shot peening and thermal treatments.

We are proud that her contributions are recognized among international leaders in the field, highlighting the impact of her research in both scientific innovation and industrial application.

The post Prof. Sara Bagherifard featured among leading women innovators in thermal spray appeared first on Archès Lab.

Excited to share our team’s involvement at the ESIAM 2026 conference in Vicenza, where we organized a symposium on Structural Integrity in Solid-State Additive Manufacturing, chaired by Sara Bagherifard, Amir Ardeshiri, Asghar Heydari, and Mario Guagliano.

We presented our new research findings and innovative developments in additive manufacturing related to ArcHIDep and Thermodust EU-funded ERC and EIC projects, with thanks to Faik Yilan, Mohammadreza Mehraban, Romario Aldrian Wicaksono, and Kemal Arslan for their valuable contributions.

Faik Yilan: Experimental investigation of WEDM process parameters for the machining of pure and heat-treated SLM AlSi10Mg

Mohammadreza Mehraban: Computational Modeling for Shape Control and Toolpath Optimization in Cold Spray Deposition for Additive Manufacturing and Welding

Romario Aldrian Wicaksono: Influence of Powder Morphology on Bonding Mechanisms in Cold-Spray Additive Manufacturing of Cu-Al2O3 Composites

Sara Bagherifard: Cold spray Additive Manufacturing of 2D Metal-Matrix Composites

Kemal Arslan: Axial crushing behavior of additively manufactured aluminum alloy Menger fractal structures: Finite element simulations

The post ESIAM 2026 conference in Vicenza appeared first on Archès Lab.

The post Best Oral Presentation Award at the International Cold Spray Conference & Expo 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.

Prof. Bagherifard shared her experience as an ERC grantee, offering valuable advice to researchers preparing future proposals.

The post Prof. Sara Bagherifard speaks at the “ERC funding opportunities” event appeared first on Archès Lab.