The study shows how deposition sequence and post-processing heat treatment influence interfacial microstructure and directional thermal conductivity. Notably, heat treatment increased the in-plane thermal conductivity of GRCop-42/IN718 joints by 131%, highlighting new opportunities for designing multi-material components for advanced thermal-management applications.

We are glad to have contributed to this collaborative work and congratulate the entire team on the publication.

The post Cold spray additive manufacturing of Inconel 718/Cu and Inconel 718/GRCop-42 multi-materials: effects of deposition sequence on interface and thermal conductivity appeared first on Archès Lab.

In our latest publication, we explored how selective ultrasonic shot peening (USP) can be used to create bi-directional microstructural heterogeneity in thin 316L stainless steel specimens. By introducing specially designed polymeric masks during USP, we selectively protected regions of the specimen surface, producing a patterned architecture composed of hardened (USPed) and untreated (masked) domains.

This approach enables microstructural gradients not only through the thickness, but also across the surface, offering a new way to tailor grain refinement, GND distribution, and strain localization through controlled surface treatment.

The results demonstrate the potential of selective USP as a versatile strategy for designing heterogeneous microstructures and tuning mechanical behavior. Our future work will focus on more advanced masking strategies and extending this concept to other material systems.

We’re excited to continue exploring how spatially controlled surface engineering can open new opportunities for microstructure design and performance optimization.

The post Engineering bi-directional microstructural heterogeneity through mechanical surface treatment appeared first on Archès Lab.

Answering this question requires looking far beyond conventional observations. In our latest research, we employed advanced large-scale molecular dynamics simulations involving millions of atoms to uncover the fundamental mechanisms governing interfacial bonding in Cold Spray.

The study combines a wide range of atomistic, metallurgical, and materials science analyses, including defect evolution, dislocation dynamics, stacking faults, deformation twinning, phase transformations, localized melting and amorphization, atomic mixing, stress and strain evolution, grain refinement, recrystallization, and crystallographic characterization. By systematically comparing FCC and BCC metallic systems, we reveal how crystal structure dictates deformation mechanisms and ultimately controls the quality of metallurgical bonding under extreme strain-rate conditions.

Beyond providing a detailed atomic-scale picture of particle impact, this work offers new insights into the physical origins of solid-state bonding and contributes to a deeper understanding of Cold Spray from a fundamental materials science perspective.

The post Atomic-scale defect-mediated interfacial bonding in cold spray: FCC vs. BCC metal systems appeared first on Archès Lab.

We are pleased to share a new publication from our group, recently published in Materials & Design. The study, “Bimetallic Al–Fe Composites with Tailored Properties Produced by Cold Spray Additive Manufacturing,” examines how retained Fe content and its spatial distribution govern densification, deformation, mechanical performance, and fracture behaviour in cold-sprayed Al–Fe composites.

By establishing a direct link between composition, microstructure, and mechanical response, the work identifies an intermediate Fe range that delivers the most favourable balance between strengthening and ductility, while revealing how particle clustering and interface-controlled damage limit performance at higher Fe contents.

Congratulations to the authors Kiran Tulasagiri Raddi, Asghar Heydari Astaraee, Sara Bagherifard.

The post Bimetallic Al–Fe composites with tailored properties produced by cold spray additive manufacturing appeared first on Archès Lab.

In this study, a high-fidelity open-source multi-particle impact framework (Free2Spray) is developed to simulate large-scale (>10,000 particles) CS deposition at a considerably convenient computational cost. The model can accurately track material changes during deposition by splitting the process into sections, each with randomly placed particles. The developed framework is proven to accurately predict the shape profiles and surface roughness of experimental single-layer single-track deposits under various nozzle scanning speeds and reproduces the experimental cross-sectional particle deformation and flattening ratios. Compared with the standard Eulerian schemes, the proposed framework reduces the computational time and memory usage by 34% and 35%, respectively.

The post Large-scale multi-particle cold spray simulation framework for deposit morphology and deformation analysis appeared first on Archès Lab.

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.