Applications in thermal and kinetic spraying increasingly aim for coating of parts with complex geometries. So far, respective robot programming for the required path during deposition is usually adjusted individually in time-consuming procedures. Thus, it is essential to develop methods that allow a fast adaptation to part geometries and production conditions as well as possible quality control. To tackle these problems, this work addresses novel strategies for robot programming and post-spray analyses. The design of the method and workflow follows routes of smart manufacturing and should enable fast and accurate implementation into spray procedures. Here, the developed application can handle complex parts of arbitrary geometry in the form of CAD files. Supported features include (i) cutting the objects according to the object boundary, (ii) creating self-intersecting curves, (iii) generating a set of index-sequence-based spatial discrete points and (iv) reordering the discrete points to generate adaptive paths. Robot offline programming allows for process simulation, analysis and optimization of the robot kinematics. By optical scanning profilometry, the layer-by-layer deposit build-up could be monitored for quality control, as well as for the determination of the final overall coating thickness. The entire procedure was tested by cold spraying onto a complex workpiece, validating the capability of the proposed strategy. Based on the universal layout of the applied methods, the strategies can also be applied for thermal spraying in general, considering individual boundary conditions. With respect to cold spraying, the implementation framework of this study provides a good basis for part repair and additive manufacturing.

Cold Spray is a solid-state Additive Manufacturing process of 3D near-net-shape parts which requires the implementation of a good spraying strategy and the choice of the right operating parameters. This paper is the result of empirical studies on the determination of the optimal processing conditions (spraying and kinematics) for the Cold Spray Additive Manufacturing (CSAM) of pure aluminum powder using a stable layers building strategy. Vertical 3D deposits (thick walls) with a height and thickness of 13-100 mm and 5-11 mm, respectively, were obtained through a series of tests that consider an effect of some kinematic parameters. The visual analysis of the deposits shows that the nozzle traverse speed as well as middle/edge pass number ratio constitute the two most influential parameters on the final shape of the deposits (flatness and straightness). All these results prove the potential of the Cold Spray Additive Manufacturing (CSAM) process as fast 3D additive method using micron sized powders, and particularly for Al powder.

In order to investigate the potentials to improve the deposition efficiency and to functionalize the polymer-based substrates, six configurations of microparticles Sn, Zn, Al, Sn+Al 2 O 3 , Al+Al 2 O 3 , Cu+Al 2 O 3 were cold sprayed on the substrate of Carbon Fiber Reinforced Polymer (CFRP) composites equipped with Cu-based sublayer or Al-based sublayer. The process conditions were kept unchanged. Microanalysis of sublayers and coatings was performed via a Scanning Electronic Microscope (SEM), the deposition mechanisms of different powders couplings on CFRP substrate were then discussed. The results indicated that although the deposition efficiencies were negative, the systems of Zn, Al and Al+Al 2 O 3 perform better among all the configurations. It was found that the addition of alumina led to a lower deposition efficiency (DE), compared to the corresponding pure coatings. For single-component Sn, Zn and Al powders, they all showed an increasing trend of DE when changing the substrate from Cu-based systems to Al-based systems. The aim of this present work is to elaborate the intrinsic causes of erosion issues and to provide a reference value for picking spraying materials and preparing functionalized CFRP substrates. According to the SEM analysis, the insufficient deformation and escape behaviours of spherical copper powders explained for the difficulty of coating formation. It was noticeable that the surfaces of Al-based systems were more uniform than those of Cu-based ones, due to their desirable deformation abilities. Besides, the significant flattened particles, material mixing and melting phenomenon were observed in Al-involved systems, which would definitely contribute to the adhesive bonding between coating and substrate.

The development of efficient ice mitigation systems for surfaces exposed to atmospheric ice has been in progress for decades. The need for passive anti-icing systems is essential as current ice mitigation systems require a substantial amount of energy and their implementation involves complex manufacturing considerations. Fluorinated polymer coatings are among the candidates for passive anti-icing systems. While many processes have been investigated to produce them, these methods can be costly, time consuming and can cause thermal damage to the substrate. The current work aims to explore a green and cheap alternative approach by using cold spray. Furthermore, the cold spray process offers advantages such as being a portable easy to perform solid-state coating process for eventual repairs. This work uses computational and experimental approaches to design and test a new dedicated nozzle for the efficient deposition of adhesive perfluoroalkoxy alkane. Computational results reveal that for the same operating conditions, the use of the new nozzle design increases particle impact temperature, improving the deposition of the feedstock material, as confirmed experimentally. The wetting behaviour, ice nucleation time and ice adhesion strength were compared for 6 different surface types, including bare aluminum, various polymer materials and the cold spray perfluoroalkoxy alkane coating on aluminium substrate. Results indicate that the as-sprayed coating performs as both a superhydrophobic and icephobic surface.

Cold spray (CS), a solid-state depositing technique, has recently demonstrated promising application in additive manufacture (AM). Compared with fusion based AM technique, cold spray can eliminate solidification defects and is appropriate to fabricate some materials that are difficult for high energy beam methods, such as Aluminium. In the cold spray process, extreme plastic deformation will occur, which triggers the severe dynamic recrystallization and result in the formation of ultrafine grain structure. For a dense CSed component, the grain structure highly influences its performance. Especially for the grain structure in the region around the impact interface, which decides the bonding of particles. However, due to the extreme processing condition of CS and complicated deformation history around the interface, it is challenging to carryout systematic study on the factors that influence the finial grain structure and make a prediction on the final grain size in this region. Here, a Monte Carlo model was built to simulate the dynamic recrystallization in the Aluminium cold spray process. The influence of impact velocity and single/multiparticle impact on the final grain structure in the interface was investigated comprehensively and independently. And the average grain size on the impact interface predicted by the modelling agreed well with reported experimental results.

Most of ductile metals can be deposited by cold spray (CS). For brittle ceramic, such solid-state deposition process is still questionable, but some recent work on Ti0 2 or hydroxyapatite powders have shown that micrometric ceramic powder could be deposited by CS. In this work, it is claimed that the nature and the porous architecture of a ceramic powder with agglomerated ultra-fine grains play an important role on the impact behaviour. The aim of this work is to investigate the deformation behaviour of ceramic agglomerated powders under high velocity impact. Two different powders, respectively 3YSZ and Y 2 O 3 , were selected in order to study their architectures (particle size, porosity, density, crystallite size, etc.). Cold spray “splats” experiments, with various spraying distances to vary the particles velocities upon impact, were carried out to observe the deformation and fragmentation. In case of Y 2 O 3 , cold spray with dynamic vacuum surrounding atmosphere up to 3kPa were also prepared to evaluate the role of the atmosphere on the resulting impact. In parallel, in situ SEM micro-compression tests at 10 −2 s −1 on cross-sectioned 3YSZ particles involving flat-punch nano-indentation and micropillar compression were performed. By modelling the compression tests, the aim is to identify a Drücker-Prager behaviour law suitable for an agglomerated ceramic powder under quasi-static compression. Such deformation behaviour could help to better understand the compaction behaviour of agglomerated powders.

The present study numerically investigates the effectiveness of co-flowing nozzles for cold spray applications. A convergent-divergent axi-symmetric nozzle system was simulated with high-pressure nitrogen flow. The particle acceleration is modelled by a two-way Lagrangian approach and validated with reference to experimental values reported in the literature. An annular co-flowing nozzle with circular central nozzle was simulated for nitrogen gas flow. The momentum preservation for central nozzle flow was observed, which results in higher particle speed for longer axial distance after nozzle exit. It is envisioned from the outcome that utilization of co-flow can lead to reduction in the divergent section length of cold spray central nozzles, which may ultimately help to address clogging issues for continuous operation. Co-flow operating at 3 MPa, same as with a central nozzle, can increase supersonic core length up to 23.8%.

The cold spray process is sensitive to variations in feedstock and requires consistent powder properties, particularly flowability, to produce uniform structures. Poor powder flow causes a cascade of effects arising from erratic feeding including deposits with void spaces and inconsistent geometries. These issues result in deposits which are not suitable for testing and prevent sample replication, hindering experimental evaluation of deposits. Powder flowability is largely affected by the material preconditioning and storage conditions; with flowability directly affecting the deposit properties of deposition efficiency (DE), porosity, and surface finish. In this study, the flowability and deposit quality of a fluoropolymer-based powder was evaluated with changing pretreatment conditions. Powder flowability was analyzed by mass flowrate (g/s), the Carr angle of repose, and the Hausner ratio. Flowability was evaluated for powders as received, after sieving (45-100 μm), with drying at elevated temperature (80 °C), with inert gas vacuum purging, and after 72 hrs. of exposure to high relative humidity (95% RH). Powders exposed to humid conditions were also dried under inert gas vacuum purging to determine the effectiveness of the process as a reconditioning method. Preconditioned powders with the highest flowability according to these tests were sealed in metal containers, stored under 95% RH for one week, and reevaluated to determine the ability of this preconditioning and storage method to protect materials from exposure to undesirable conditions. Next, the effect of preconditioning on cold spray deposit quality was evaluated for the fluoropolymer-based powder with the best and worst flowability. The choice of spray conditions was informed by simulation of particle velocity and temperature distribution at impact using one-dimensional compressible flow modeling, couple with thermal analysis of the powder. The DE was determined gravimetrically, surface roughness was evaluated using a profilometer, and microstructure was evaluated using a scanning electron microscope (SEM). The ability to manipulate powder flowability through simple preconditioning methods and quickly evaluate the properties of the feedstock before use in the manufacturing process, coupled with straightforward and rapid evaluation the resultant deposit; will save time and money, and accelerate research efforts, compared to evaluating powder suitability by trial and error.

Aerosol cold spraying (ACS) is modification of low-pressure cold spray technology which allows to deposit ceramic and metal-based coatings. ACS technology in vacuum possesses the formation of films from sub-micro and nanoparticles directly at room temperature. The ACS technology is still under development to cover more application and discover solutions of spraying different kind of powders on different types of material substrate and optimizing spraying conditions to obtain the best results. The main objective of the present work is to develop a new ACS cold spray technology of Hydroxyapatite (HA) and Copper powder deposition onto both the implants and ceramic substrates. The new AD spraying system with radial injection of particles to be deposited is constructed and tried. An influence of technology parameters on the coating structure and properties are presented. In addition, the combined cold spray and sintering technology technique is further investigated for additive manufacturing applications.

Cold spray is a high-speed solid state deposition technique which allows for fabricating coatings and free standings structures by careful tuning of process parameters. However due to the complex dependencies of the process parameters a machine learning approach is utilized in this work to predict the coating properties. A machine learning (ML)-based data driven platform for determining the deposition porosity for cold sprayed deposition with the goal of reducing product cost and time has been developed. In this work, five ML models - Linear Regression, Decision Tree Regression and Random Forest Regression, XGBRegressor and LGBMRegressor are compared for the prediction of coating porosity from spray and material parameters namely Heat Capacity Ratio of carrier gas, Processing Gas Temperature, Processing gas pressure, Standoff Distance, Average Powder Diameter, Powder Material Density, and the Substrate density. A total number of 227 data sets were extracted from an extensive literature survey on cold spray deposition of metal/alloy powders which were used to train the ML models. The data analysis showed strong and weak correlations of several processing parameters with the coating porosity. The processing gas temperature and pressure have a negative and average powder diameter has a positive correlation with the deposition porosity. The coating porosities of 10 unknown sets (which were not included in the training or the validation data sets) of processing parameters predicted by the trained algorithms were compared with each other. Decision Tree regression algorithm showed the most appropriate predictions with R2 Fit of 0.75 and MAE of 2.93, while the Linear Regression model did the worst predictions with R2 Fit of 0.27 and MAE of 5.064.