In-flight Particle Characteristics Research Articles

Polymer Metallization by Cold Spray Deposition of Polyamide-Copper Composite Coatings

Cold spray metallization of polymers is a promising surface engineering technique that enables the deposition of metal coatings onto polymer substrates at low process temperatures, resulting in improved surface properties, thus enhanced functionality of the polymeric material. However, deposition of well-adhering metallic coatings without causing surface damage to the polymer substrate is still a challenge. In this work, copper-polyamide composite coatings with different copper concentrations between 30 and 75 vol.% in the starting powders were deposited on polyamide substrates using a low-pressure cold spray system with two nozzle geometries of short and long diverging sections. The spray parameters were first developed for the deposition of polyamide powder (at gas temperature of 260 °C and gas pressures ranging from 0.41 to 1.37 MPa), and then used to spray the composite powder mixtures where the polyamide particles were acting as a binder for copper particles. Inflight and impact particle characteristics (velocity and temperature) of the polyamide powder were simulated to better understand the deposition properties. Considering that the selected conditions were suboptimal for the deposition of copper particles, no surface damage was caused as no penetration of the copper particles into the polymer substrate occurred. The results show that increasing the copper content in the powder mixtures significantly improved the resulting coating uniformity and the retained copper content. In addition, the coating deposited by spraying the powder mixture with a higher copper content of 75 vol.% and using the longer nozzle yielded the highest cohesion strength. To further improve coatings cohesion, two post-spray processing methods of furnace heating and hot pressing were used, and the effect of each process on coatings properties was investigated.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Given name: [Kintak Raymond] Last name [Yu]. Also, kindly confirm the details in the metadata are correct.The author names are now correct: Kintak Raymond given name and Yu last nameAll other details are corrects

Active learning-driven uncertainty reduction for in-flight particle characteristics of atmospheric plasma spraying of silicon

The first-of-its-kind use of the active learning (AL) framework in thermal spray is adapted to enhance the prediction accuracy of the in-flight particle characteristics. The successful AL framework implementation via Bayesian Optimisation is beneficial in, first, reducing the maximum uncertainty, which greatly improves the prediction accuracy and informativeness of the existing database. Second, it reduces local uncertainty around a contrived test point that offers the capability to find improvement in a limited search area, allowing an accurate prediction around a desired test point. The dataset for Machine Learning (ML) training consists of 26 atmospheric plasma spray (APS) parameters of silicon and a further six AL-guided spray runs carried out to reduce maximum uncertainty in the initial database. On average, a 52.9% improvement (error reduction) of RMSE and an R2 increase of 8.5% were reported on the predicted in-flight particle velocities and temperatures after the AL-driven optimisation. Furthermore, the contrived test point optimisation to predict the best possible characteristics in a limited search space resulted in a three-fold increase in prediction accuracy compared to the non-optimised prediction. The AL-driven optimisation proved to be greatly beneficial for resource-intensive thermal spraying, as the framework not only allowed an accurate prediction of the in-flight particle characteristics but also found expected improvement around a desired in-flight characteristic. Furthermore, the framework uses the Gaussian Process (GP) ML model as a surrogate that generalises a global solution without necessarily involving physical and underlying mechanisms, thus extending the framework to other thermal spraying methods.

Influence of in-flight particle characteristics and substrate temperature on the formation mechanisms of hypereutectic Al-Si-Cu coatings prepared by supersonic atmospheric plasma spraying

Hypereutectic Al-Si-Cu coatings were prepared by supersonic atmospheric plasma spraying to enhance the surface performance of lightweight alloys. To find out optimum process conditions and achieve desirable coatings, this work focuses on the influence of three important parameters (in-flight particle temperature, impact velocity, and substrate temperature) on the collected splats morphology, coatings microstructure and microhardness. Results show that appropriate combinations of temperature and velocity of in-flight particles cannot only completely melt hypereutectic Al-Si-Cu particles, especially the primary Si phase, but also provide the particles with sufficient kinetic energy. Thus, the optimized coating consists of 98.6 % of fully-melted region with nanosized coupled eutectic and 0.9 % of porosity. Increasing the substrate deposition temperature promotes the transition from inhomogeneous banded microstructure to homogeneous equiaxed microstructure with a lower porosity level. The observations are further interpreted by a newly developed phase-change heat transfer model on quantitatively revealing the solidification and remelting behaviors of several splats deposited on substrate. Besides, phase evolutions including the formation of supersaturated α-Al matrix solid solution, growth of Si and Al2Cu phases at different process conditions are elaborated. An ideal microstructure (low fractions of unmelted/partially-melted regions and defects) together with solid solution, grain refinement, and second phase strengthening effects contributes to the enhanced microhardness of coating. This integrated study not only provides a framework for optimizing Al-Si based coatings via thermal spraying but also gives valuable insights into the formation mechanisms of this class of coating materials.

Novel liquid fuel HVOF torches fueled with ethanol: relationships between in-flight particle characteristics and properties of WC-10Co-4Cr coatings

Commercial WC-10Co-4Cr powder was sprayed by a novel HVOF process with ethanol used as fuel. Ethanol benefits from environmentally friendly features and less consumption of oxygen compared to conventional fossil liquid fuels (i.e. kerosene). Investigations were carried out to determine the influence of different oxygen/fuel ratios on the evolution of the velocity and temperature of in-flight particles in correlation with the properties of the resulting WC-CoCr coatings. The phase compositions, microstructures, porosity data, microhardness and fracture toughness were tested for comparation. The velocity and temperature of particles strongly depend on the total flow rate of oxygen and fuel. The variation of the ethanol flow rate has a more obvious influence on the velocity and temperature of the particles than that of oxygen. Detailed correlation of particle parameters and the coating properties are elaborated in order to deduce particle parameter ranges providing the coatings exhibiting optimum properties.

Influence of spray gun parameters on inflight particle's characteristics, the splat-type distribution, and microstructure of plasma-sprayed YSZ coatings

Ceramic coatings, fabricated with specific properties using the atmospheric plasma spray (APS) process, are widely used for many applications in which the porosity and splat interfaces are the main factors affecting the performance. Since the coating microstructure is composed of large numbers of molten and semi-molten particles impinged successfully at the substrate (known as splats), the produced coatings are characterized by the melting degree of these particles and their relative splat-type fractions.In the present work, the effect of process parameters settings has been studied systematically, relating the characteristics of impinging particles to splat formation and eventually to microstructure development and properties of the coating. Therefore, individual splats were collected on mirror-polished substrates and observed using image analysis (IA). These were evaluated and categorized into different splat types, based on their melting degree and morphology, under each combination of spray conditions. It was found that gun current and standoff distance have a profound effect on the characteristics of impinging particles. These, in turn, determine the relative fractions of splat types, layered structure, and final properties of the deposit. The effect of splat-type distribution on the bonding strength between layers, lamellar structure, and coating porosity was investigated.

Particle temperature effect in cold spray: A study of soft particle deposition on hard substrate

In cold spray, the interactions between feedstock particles and propellant gas control deposition outcome. The particle impact temperature, along with impact velocity, governs the deposition due to its effect on the impact deformation processes. As bonding mechanisms rely primarily on the particle/substrate plastic flow upon impact, particle impact temperature holds a predominant role in process optimization.The current work studies the effect of pure aluminum particle impact temperature on deposition phenomena when propelled onto a hard steel substrate. Particle impact temperature was varied while achieving the same particle impact velocity, using low and high-pressure cold spray systems. Three different particle diameters are studied, with varying resulting inherent impact temperatures, to provide size dependent insight on deposition/adhesion.Experimental results demonstrate that at low particle impact temperature, i.e. 50 °C, coating adhesion is controlled by in-situ peening. Adhesion shifts to particle in-flight characteristics dependent bonding, i.e. η = V/Vc, as particle temperature increases. At low particle impact temperatures, the coating bond strength increases with increasing in-situ impingement intensity and frequency, i.e. decreasing deposition efficiency with increasing particle size. At higher particle impact temperatures, in-situ peening rate decreases and the adhesion strength increases with decreasing particle size. In addition to affecting the impact process (DE, Vc and adhesion), the particle impact temperature also influences the coating mechanical properties through the creation of temperature-dependent crack propagation mechanisms, i.e. deflection, bridging and acceleration, and of particle-particle structural arrangement related to deformation processes, i.e. fish scale effect and pseudo-plasticity behavior.

Prediction of control parameters corresponding to in-flight particles in atmospheric plasma spray employing convolutional neural networks

Optimization of control parameters for plasma spraying process is of great importance in thermal spray technology development. Engineers may limit themselves to local optimal solution by considering countable potential design solutions when selecting the plasma spray parameters in practice. This work proposes one decision support model by employing convolutional neural network (CNN) to explore the suitability of preliminary design. The approach aims to help engineers select global optimal solution with short time and low labor cost by invoking the models' capability of extracting potential features of in-flight particle characteristics. Simulation results make it possible to analyze new spraying process and train the designed model under the condition of insufficient experience and data. Firstly, the distributions of particle status obtained from simulation results act as the input and the control parameters are the output. Secondly, the projections between the in-flight particles and the control parameters are built implicitly and analyzed through CNN models. Thirdly, we validate the statistical information of particle state distributions through visualizing the feature maps and filters. Finally, the trained CNN models are verified by the fitted Gaussian distributions with basically consistent results. By building projections of in-flight particles and control parameters, new entrants and apprentices are capable of deducing the control parameters with the help of the pre-trained CNN model, thus cutting down the threshold for new practitioners.

Analysis of Scattering Light from In-flight Particles in Suspension Plasma Spray for Size Measurement

Suspension plasma spray (SPS) is an emerging coating process for making surfaces with superior properties. In this process, in-flight spray particle characteristics such as size, velocity, and temperature have a direct influence on the properties of the deposited coatings. Accordingly, online diagnostic tools to characterize the in-flight particles in the SPS are sought by research laboratories and industrial centers for process optimization and control. However, small particle size, high temperature, and radiation of the plasma make it challenging to carry out these measurements. In this study, we used a light diffraction (LD) approach to measure online the size of in-flight particles sprayed from a well-predefined size distribution. Laser beam refraction by the hot plasma/gas jet is one of the main sources of noise for such size measurement. Successful measurements were achieved by shielding the measurement section and filtering the plasma radiation to reduce the influence of the laser refraction and plasma radiation. Results showed that the LD method has the potential to be used to monitor online the size distribution of in-flight particles in the SPS process.

Optimizing Thermoelectric Properties of In Situ Plasma-Spray-Synthesized Sub-stoichiometric TiO2−x Deposits

In this article, an attempt has been made to relate the thermoelectric properties of thermal spray deposits of sub-stoichiometric titania to process-induced phase and microstructural variances. The TiO2−x deposits were formed through the in situ reaction of the TiO1.9 or TiO1.7 feedstock within the high-temperature plasma flame and manipulated via varying the amounts of hydrogen fed into in the thermal plasma. Changes in the flow rates of H2 in the plasma plume greatly affected the in-flight particle behavior and composition of the deposits. For reference, a high-velocity oxy-fuel spray torch was also used to deposit the two varieties of feedstocks. Refinements to the representation of the in-flight particle characteristics derived via single particle and ensemble diagnostic methods are proposed using the group parameters (melting index and kinetic energy). The results show that depending on the value of the melting index, there is an inverse proportional relationship between electrical conductivity and Seebeck coefficient, whereas thermal conductivity has a directly proportional relationship with the electrical conductivity. Retention of the original phase and reduced decomposition is beneficial to retain the high Seebeck coefficient or the high electrical conductivity in the TiO2 system.

Numerical Study on Plasma Jet and Particle Behavior in Multi-arc Plasma Spraying

Plasma jet and particle behavior in conventional single-arc plasma spraying has been subject to intensive numerical research. However, multi-arc plasma spraying is a different case which has yet to be investigated more closely. Numerical models developed to investigate the characteristics of multi-arc plasma spraying (plasma generator, plasma jet, and plasma–particle interaction models) were introduced in previous publications by the authors. The plasma generator and plasma jet models were already validated by comparing calculated plasma temperatures with results of emission spectroscopic computed tomography. In this study, the above-mentioned models were subjected to further validation effort. Calculated particle in-flight characteristics were compared with those determined by means of particle diagnostics and high-speed videography. The results show very good agreement. The main aim of the current publication is to derive conclusions regarding the general characteristics of plasma jet and particle in-flight behavior in multi-arc plasma spraying. For this purpose, a numerical parameter study is conducted in which the validated models are used to allow variations in the process parameters. Results regarding plasma jet/particle in-flight temperatures and velocities are presented. Furthermore, the general characteristics of plasma jet and particle behavior in multi-arc plasma spraying are discussed and explained. This contributes to better understanding of the multi-arc plasma spraying process, in particular regarding the injection behavior of particles into hot regions of the plasma jet. Finally, an example test case showing a possible practical application area of the models is introduced.

Numerical Coupling of the Particulate Phase to the Plasma Phase in Modeling of Multi-Arc Plasma Spraying

Inherent to Euler-Lagrange formulation, which can be used in order to describe the particle behavior in plasma spraying, particle in-flight characteristics are determined by calculating the impulse, heat and mass transfer between the plasma jet and individual powder particles. Based on the assumption that the influence of the particulate phase on the fluid phase is insignificant, impulse, heat and mass transfer from particles to the plasma jet can be neglected using the so-called numerical approach of “one-way coupling”. On the other hand, so-called “two-way coupling” considers the two-sided transfer between both phases. The former is a common simplification used in the literature to describe the plasma-particle interaction in thermal spraying. This study focuses on the significance of this simplification on the calculated results and shows that the use of this simplification leads to significant errors in calculated plasma and particle in-flight characteristics in three-cathode plasma spraying process.

Modelling the Plasma Jet in Multi-Arc Plasma Spraying

Particle in-flight characteristics in atmospheric plasma spraying process are determined by impulse and heat energy transferred between the plasma jet and injected powder particles. One of the important factors for the quality of the plasma-sprayed coatings is thus the distribution of plasma gas temperatures and velocities in plasma jet. Plasma jets generated by conventional single-arc plasma spraying systems and their interaction with powder particles were subject matter of intensive research. However, this does not apply to plasma jets generated by means of multi-arc plasma spraying systems yet. In this study, a numerical model has been developed which is designated to dealing with the flow characteristics of the plasma jet generated by means of a three-cathode spraying system. The upstream flow conditions, which were calculated using a priori conducted plasma generator simulations, have been coupled to the plasma jet simulations. The significances of the relevant numerical assumptions and aspects of the models are analyzed. The focus is placed on to the turbulence and diffusion/demixing modelling. A critical evaluation of the prediction power of the models is conducted by comparing the numerical results to the experimental results determined by means of emission spectroscopic computed tomography. It is evident that the numerical models exhibit a good accuracy for their intended use.

Investigation of particle characteristics, composition and microstructure of LaxCe1−xO2−x/2 thermal barrier coatings during supersonic atmospheric plasma spray using Box–Behnken design

Abstract In this work, Box–Behnken design was used for investigating the influence of the main operating parameters (Argon flow rate: Ar , hydrogen flow rate: H 2 and arc current: I ) on velocity and temperature of La 2 Ce 2 O 7 in-flight particle characteristics (i.e., mean velocity and temperature) during supersonic atmospheric plasma spraying (SAPS). The relationship between the particle characteristics and the composition and the microstructure of LC coating was investigated eventually. During spraying, the particle temperature and velocity were monitored by on-line diagnosis system. The results indicated that the particle velocity linearly increased with the increase of Ar , H 2 and I . I and the interaction between I and Ar and/or H 2 were the most important parameters which influenced the particle temperature. The content of Ce 4 + in the as-sprayed coating rapidly decreased when the particle temperature increased from 2923 to 3036 °C, and then slowly decreased in the range of 3127–3218 °C. When the particle velocity fluctuation was less than 5%, the porosity of as-sprayed coatings decreased with the increase of particle temperature, however, it increased with the increase of particle velocity.

Effect of Substrate and Its Shape on in-Flight Particle Characteristics in Suspension Plasma Spraying

Obtaining a uniform coating on curved mechanical parts such as gas turbine blades is one of the industrial challenges in suspension plasma spraying. Through a three dimensional numerical analysis, this study is aimed at providing a better understanding of the effect of substrate curvature on in-flight particle temperature, velocity, and trajectory. The high temperature and high velocity plasma flow is simulated inside the plasma torch using a uniform volumetric heat source in the energy equation. The suspension of yttria-stabilized zirconia particles is molded as a multicomponent droplet while catastrophic breakup regime is considered for simulating the secondary break up when the suspension interacts with the plasma flow. A two-way coupled Eulerian-Lagrangian approach along with a stochastic discrete model was used to track the particle trajectory. Particle size distribution in the vicinity of the substrate at different stand-off distances has been investigated. The results show that sub-micron particles obtain higher velocity and temperature compared to the larger particles. However, due to the small Stokes number associated with sub-micron particles, they are more sensitive to the change of the gas flow streamlines in the vicinity of a curved substrate.

Modular implementation of artificial neural network in predicting in-flight particle characteristics of an atmospheric plasma spray process

This paper presents a modular implementation of an artificial neural network to model the atmospheric plasma spray process in predicting the in-flight particle characteristics from the input processing parameters. The in-flight particle characteristics influence the structure and properties of the thermal spray coating and, thus, are considered important parameters to comprehend, simulate and predict the manufacturing process. The modular implementation allows simplification of the optimized model structure with enhanced ability to generalise the network. As well, the underlying relationship between each of the output in-flight characteristics with respect to the input processing parameters is explored. Smaller networks are constructed that achieves better, or in some cases, similar results. The training process is found to be more robust and stable along with fewer fluctuations in the values of the network parameters. The networks also respond to the variations of the number of hidden layer neurons with some definite trend. The predictable trend enhances reliability of the application of the artificial neural network in modelling the atmospheric plasma spray process and overcomes the variability and non-linearity associated with the process.

Influence of in-flight particle state diagnostics on properties of plasma sprayed YSZ-CeO2 nanocomposite coatings

This article describes the influence of controlling in-flight hot particle characteristics on properties of plasma sprayed nanostructured yttria stabilized zirconia (YSZ) coatings. This article depicts dependence of adhesion strength of as-sprayed nanostructured YSZ coatings on particle temperature, velocity and size of the splat prior to impact on the metallic substrate. Particle temperature measurement is based on two-color pyrometry and particle velocities are measured from the length of the particle traces during known exposure times. The microstructure and adhesion strength of as-sprayed nano-YSZ coatings were studied. Field emission scanning electron microscopy results revealed that morphology of coating exhibits bimodal microstructure consisting of nano-zones reinforced in the matrix of fully melted particles. The coating adhesion strength is noticed to be greatly affected by the melting state of agglomerates. Maximum adhesion strength of 42.39 MPa has been experimentally found out by selecting optimum levels of particle temperature and velocity. The enhanced bond strength of nano-YSZ coating may be attributed to higher interfacial toughness due to cracks being interrupted by adherent nano-zones.

The Influence of Spraying Parameters on Stresses and Mechanical Properties of HVOF-Sprayed Co-Cr-W-C Coatings

Stellite 6 Co-Cr-W-C coatings were sprayed by HVOF while systematically varying the spraying parameters, namely the equivalent ratio and combustion pressure. During spraying, the in-flight particle temperature and velocity were measured. Deposition, thermal and residual stresses were determined by in-situ curvature monitoring of the sprayed samples. Young's moduli and hardness of the coatings were determined by instrumented indentation. The relationship between spraying parameters, in-flight particle characteristics and mechanical properties is discussed.

Relationships between in-flight particle characteristics and properties of HVOF sprayed WC-CoCr coatings

Commercially available WC10Co4Cr powder was thermally sprayed by HVOF process. The methane was used as the fuel gas and its flow rate was successively changed as well as the oxygen. The investigation was carried out to determine the influence of operating parameters on the evolution of velocity and temperature of in-flight particles in order to have a better understanding of the interaction between the particle and the flame jet. In relation to the particle characteristics, properties of the sprayed coatings were examined in terms of microstructure, porosity level and microhardness. The results show that the particle velocity and temperature depends strongly on the particle size. The variation of the methane flow rate has a more obvious influence on the velocity and temperature of particles than that of the oxygen. The changes of porosity and microhardness of deposited coatings are discussed corresponding to the variation of fuel and oxygen flow rates.

Plasma Spray Process Operating Parameters Optimization Based on Artificial Intelligence

During plasma spray process, many intrinsic operating parameters allow tailoring in-flight particle characteristics (temperature and velocity) by controlling the plasma jet properties, thus affecting the final coating characteristics. Among them, plasma flow mass enthalpy, flow thermal conductivity, momentum density, etc. result from the selection of extrinsic operating parameters such as the plasma torch nozzle geometry, the composition and flow rate of plasma forming gases, the arc current intensity, beside the coupled relationships between those operating parameters make difficult in a full prediction of their effects on coating properties. Moreover, temporal fluctuations (anode wear for example) require “real time” corrections to maintain particle characteristic to targeted values. An expert system is built to optimize and control some of the main extrinsic operating parameters. This expert system includes two parts: (1) an artificial neural network (ANN) which predicts an extrinsic operating window and (2) a fuzzy logic controller (FLC) to control it. The paper details the general architecture of the system, discusses its limits and the typical characteristic times. The result shows that ANN can predict the characteristics of particles in-flight from coating porosity within maximal error 3 and 2 % in temperature and velocity respectively. And ANN also can predict the operating parameters from in-flight particle characteristics with maximal error 2.34, 4.80 and 8.66 % in current intensity, argon flow rate, and hydrogen flow rate respectively.

An Extreme Learning Machine Algorithm to Predict the In-flight Particle Characteristics of an Atmospheric Plasma Spray Process

A robust single hidden layer feed forward neural network (SLFN) is used in this study to model the in-flight particle characteristics of the atmospheric plasma spray (APS) process with regard to the input processing parameters. The in-flight particle characteristics influence the structure and properties of the APS coating and, thus, are considered important parameters to comprehend the manufacturing process. The training times of traditional back propagation algorithms, mostly used to model such processes, are far slower than desired for implementation of an on-line control system. Use of slow gradient based learning methods and iterative tuning of all network parameters during the learning process are the two major causes for such slower learning speed. An extreme learning machine (ELM) algorithm, which randomly selects the input weights and biases and analytically determines the output weights, is used in this work to train the SLFNs. Performance comparisons of the networks trained with ELM algorithm and standard error back propagation algorithms are presented. It is found that networks trained with ELM have good generalization performance, much shorter training times and stable performance with regard to the changes in number of hidden layer neurons. The trends represent robustness of the trained networks and enhance reliability of the application of the artificial neural network in modelling APS processes.