Search Results for artificial intelligence

The very singular pore network architecture of thermal spray coating results from the combination of several phenomena occurring during the coating buildup process. Close pores, usually of large dimensions, result from stacking defaults when impinging particles spread onto previously deposited layers. Intra-lamellar cracks, perpendicular to the substrate surface, develop mostly in ceramic lamellae during solidification. Inter-lamellar cracks, parallel to the substrate surface, depend significantly from the surface tension characteristics during the particle spreading stage. One way or another, these characteristics are related to the processing parameters implemented to manufacture the coating and they significantly modify the coating characteristics, their cohesion, their compliance and their impermeability, among the most significant. Al 2 O 3 -TiO 2 (13% wt.) coatings were atmospheric plasma sprayed implementing several sets of processing parameters, among which power parameters (i.e., arc current intensity, plasmas gas flow rates, etc.), feedstock injection parameters (i.e., carrier gas flow rate, injector internal diameter, etc.) and environment parameters (i.e., spray angle, etc.) were varied. Pore contents were analyzed implementing image analysis. Pore network connectivity was analyzed implementing an electrochemical test: the higher the passivation potential of the substrate, the higher the coating pore network connectivity.

During plasma spray process, many intrinsic operating parameters allow tailoring the in-flight particle characteristics (temperature and velocity), thus affecting the final coating characteristics. Among them, plasma enthalpy, thermal conductivity, momentum, density, etc. result from the selection of extrinsic operating parameters such as the plasma torch nozzle geometry, composition and flow rate of plasma forming gases, the arc current intensity, etc. The complex relationships among those operating parameters make it difficult to fully predict their effects. Moreover, temporal fluctuations (anode wear for example) require "real time" corrections to maintain particle characteristics to targeted values. In addition, substrate temperature has to be maintained to targeted values depending upon the feedstock to be sprayed, the geometry of the part to be coated, its thermal capacity, etc. An expert system was 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 typical characteristics. An example is finally presented.

Thermal spraying is a complex physical process consisting of three main sub-systems: flame/plume generation, powder/flame/plume interaction and coating build-up. While mathematical and CFD models provide valuable insight about the individual modules of a thermal spray process, it is very difficult to gain overall insight of the whole process and dependencies between different inputs and outputs using mathematical and CFD analysis, due to very complex and interconnected nature of the thermal spray process. In this work, a sophisticated experiment has been conducted to collect enough data for the sake of developing data-driven model of a plasma spray process. Metco 204 powder feedstock material and F4 gun have been used. An optimized number of data samples has been chosen by applying common industrial input parameters in the experiment. The developed neural network model is able to predict the coating quality parameters with acceptable average accuracy of above 90% on test data by considering all relevant measurement error deviations of the process analysis methods. A sophisticated user-interface has been developed to enable the use of the model for coating parameter development as well as the designing recipe for target coating characteristics. The developed model can be used for different purposes: parameter development, off-line coating quality control, and eventually adaptive coating control.

Parametric drifts and fluctuations occur during plasma spraying. These drifts and fluctuations originate especially from the electrode wear and intrinsic plasma jet instabilities: the plasma net power varies in this case modifying significantly its thermodynamic properties and transport coefficients hence modifying the momentum and heat transfers to the particles. It is possible to control the in-flight particle characteristics by adjusting continuously the operating parameters, in particular the power parameters. Due to the large amplitudes of these drifts and fluctuations, the strategy to adopt will depend on the required corrections to apply to the particle characteristics. Developing a reliable controller requires: (i) implementation of reliable sensors to accurately diagnose in-flight particle characteristics but also some other parameters, such as surface temperature during spraying; (ii) development of a robust command, to insure the stability of the control system. Fuzzy logic permits to define parametric correction rules and the command can be based on these algorithms; (iii) linking of the robust command to a predictive model. Artificial neural networks, among other artificial intelligence protocols such as genetic algorithms, proved to be able to predict in-flight particle and coating characteristics; (iv) validation of the corrections with an extensive database used as a reference. Coupling neural protocols to fuzzy logic should permit the development of such an independent controller which could adjust in real time the operating parameters as a function of the measured in-flight particles to manufacture a coating with identical conditions among its entire thickness. This paper aims at presenting the methodology and the controller and at simulating the spray operation.

This study employs an XAI framework to gain insights into Residual Network and Artificial Neural Network models trained on both simulations and experimental data to predict deposition efficiency (DE) in atmospheric plasma spraying (APS). SHapley Additive exPlanations (SHAP), an interpretability framework, was then applied to help identify which process parameters have the most significant influence on the DE and to reveal how changes in specific parameters affect the DE by elucidating their impact on the model predictions.

In this work, an artificial neural network (ANN) model was developed to investigate the application of Cr 3 C 2 -25NiCr coatings by HVOF spraying and predict the resulting properties based on flow rates, stand-off distance, and other parameters. HVOF coatings were sprayed and tests were conducted to generate data for training, validating, and testing the model. The model was trained with an R-value of 0.99965 to predict the relationship between spray parameters and coating properties including hardness, porosity, and wear rate. The reliability and accuracy of the model was subsequently verified using independent test sets.

Machines and other equipment that are used in industry are generally long-term investments. Once invested, the machines usually run for several decades until a technical reinvestment is necessary. Nowadays, the 4th industrial revolution is taking place and digitalization is omnipresent. Big Data, Internet of Things (IoT) and Artificial Intelligence (AI) are just some of the technological innovations that are shaping the future right now. These innovations do not work with the manually driven equipment that has been invested some years ago because they are all based on data – and these (partly) manually operated equipment don´t produce data or they don´t collect it properly. In order to produce and collect data, the manually working machines have to be replaced – or retrofitted! voestalpine Stahl GmbH and its technical service and maintenance department (TSM, furthermore short voestalpine) decided to retrofit the existing equipment based on technological, economical as well as sustainability reasons. The approach, advantages and limitations are displayed here.

Parametric drifts and fluctuations occur during plasma spraying. These drifts and fluctuations originate primarily from electrode wear and intrinsic plasma jet instabilities. One challenge is to control the manufacturing process by identifying the parameter interdependencies, correlations and individual effects on the in-flight particle characteristics. Such control is needed through methods that (i) consider the interdependencies that influence process variability and that also (ii) quantify the processing parameter-process response relationships. Artificial intelligence is proposed for thermal spray applications. The specific case of predicting plasma power parameters to manufacture grey alumina (Al 2 O 3 -TiO 2 , 13% by wt.) coatings was considered and the influence of the plasma spray process on the in-flight particle characteristics was investigated.

Wire-arc spraying is particularly used for large-area coatings due to the high cost efficiency of the process but is also characterized by strong fluctuations. Nowadays, a costly and time-consuming inspection is required after coating in order to identify and eliminate possible coating defects caused by the process instability. Therefore, a sensor unit with seven channels is established, which realizes an in situ monitoring of the process. The voltage and current sensors are analyzed in detail within this work. Additionally, a variation of the process parameters voltage and wire feed was used to compare the data of a stable and an instable process regarding the arc stability. For a deeper understanding of the process and its performance, the surface is characterized by confocal laser scanning microscopy and cross-sections are investigated by SEM as well as light microscopy. The new and so far, unique sensor unit is successfully established for the current and the voltage sensor on the wire-arc spraying process. The in situ recording identifies fluctuations of the spraying process. Anomalies of the current I were detected before the break down of the arc occurred. The parameter variation showed an influence on the coating properties. A higher voltage results in a denser coating structure.

On-line process control appears today as a potential way to improve thermal spray coating quality and reliability. In order to reach that goal, several requirements are needed. Firstly, reliable sensors have to be developed to accurately diagnose in-flight particle characteristics but also some other parameters, such as coating surface temperature during spraying. Secondly, a robust command, linked to a model, is required to insure the stability of the control system. Finally, an extensive and representative database is required as reference. Several methodologies can be implemented to define such a structure. Artificial Neural Networks (ANNs) and neuromimetic models, based for example on fuzzy logic, appear as an interesting way with a large potential of improvement to control non-linear systems, such as the thermal spray processes.

As an emerging additive manufacturing method, cold spray additive manufacturing (CSAM) has attracted more and more researchers’ attention due to its unique advantages. However, only a few researchers have studied the fabrication of complex structural components. Therefore, it is important to develop a general CSAM framework that is suitable for the fabrication of different shapes of workpieces. In particular, the choice for the optimal kinematic spraying parameters, the prediction of deposit evolution and the planning of spraying trajectory are the most basic and crucial. Different sub-modules are integrated in the proposed framework to solve these problems. In detail, the modeling methodology is used to obtain the optimal kinematic spraying parameters and to predict the deposit evolution in the simulation. Based on the feasible parameters, the trajectory planification methodology is used to generate the spraying trajectory for the workpiece being manufactured, especially the workpiece with complex structure. Finally, the simulation and experimental results of a fabrication for a workpiece with complex structure provide the developed system is reliable and effective. The framework developed in this paper can considered as a general tool for additive manufacturing of with complex structural workpieces in the CSAM.

Traditionally, large moulds for manufacturing of CFRP (carbon fiber reinforced plastics) parts are machined from a solid metal block making this way of manufacturing very energy and time consuming. Using wire arc spraying thin-walled moulds can be produced by spraying onto an original mould and separating the coating. In order to create a reliable and high quality product the manufacturing process needs to be highly reproducible. Thus the spraying process requires monitoring and control, which can be done using artificial neural networks (ANN). In our approach, for monitoring the process the diagnostic system PFI (Particle Flux Imaging) is used to characterize the spray particle stream, which is essentially achieved by fitting an ellipse to an image of the particle stream. Comparing deviations from a reference ellipse recorded for an “optimal” coating process provides data that can subsequently be used for process control. Investigations performed by the method of design of experiments (DOE) show a very strong correlation of the parameters pressure, current, and voltage with certain parts of the PFI data: for example the semi-minor axis of the ellipse depends linearly on voltage and current but quadratic on pressure. These results can further on be used to control the coating process by ANN. This paper discusses the application of this method and its feasibility for industrial use.

The use of numerical modeling in the development and optimization of thermal spray processes has been hampered by the complexities of the process, with up to 50 interdependent variables involved, and the need to identify and explicitly define each relationship. This work demonstrates an alternative modeling approach that employs neural network learning algorithms. Plasma spraying was selected as the test case because it presents the greatest challenge in terms of processing parameters and the magnitude of their effect on layer quality. This paper provides an introduction to neural networks and its use in thermal spraying. A companion paper in these same proceedings presents an example in which the method is used to link processing parameters with in-flight particle characteristics in a dc plasma jet. Paper includes a German-language abstract.

Gas-fuel HVOF for thermal spraying of WC-CoCr powder is widely known and well described in literature. Focus are the various influencing factors like fuel-to-oxygen ratio, standoff-distance and powder feed rate on the coating characteristics like hardness and porosity. However, the total gas flow is usually not being described in this context despite its wide influence on particle characteristics and therefore on coating properties. In this study, the characteristic influence of the total gas flow on roughness, hardness and porosity is described as well as its effect on the particle characteristics. The study performed was based on technical standard values for thermally spraying WC-Co-Cr via gas-fuel HVOF (DJ2700 hybrid) and additional trials for increased and decreased total gas flow. It was possible to determine that with higher gas flow the deposition rate increases while the roughness and porosity decrease. However, these results cannot be viewed in isolation as other factors, such as the fuel-to-oxygen ratio, are affecting the particle and coating characteristics at the same time. Therefore, the total gas flow is also considered in combination with other factors.

In the High Velocity Oxygen Fuel (HVOF) technology, the coating properties are sensitive to the behaviors of in-flight particles, which are mainly influenced by the processing parameters. However, due to the complex chemical and thermodynamic reactions, the real-time optimization of the coating properties during the HVOF process is still a challenging issue. This study focused on establishing an Artificial Neural Networks (ANN) model to analyze the influence of the processing parameters on the characteristics of in-flight particles. Hydroxyapatite (HA) powders were selected to deposit onto the stainless steel substrates via an improved HVOF spraying system. Combined with an Accuraspray-g3 system applied to acquire the temperature and velocity of inflight HA particles, the artificial neural network algorithm was well trained to predict the velocity and temperature of in-flight particles. The relationship between the variations of the operating parameters (gas flow rates and fuel-to-oxygen ratio) and the behaviors of in-flight HA particles was investigated, which therefor contributes to analyzing and optimizing the mechanical performance and crystallinity of the HA coatings.

This study had for objective to build a deposit mathematical model to be interfaced with robotic simulation to predict some coating properties. The deposit model is based on a database considering the effects of the spray angle, the spray distance, the relative torch – substrate speed, the number of passes and the position of the powder injector relative to the torch trajectory. The deposit geometry, its porosity level, its hardness and its apparent Young modulus were the considered deposit geometrical and structural attributes. The processing parameters – deposit attributes relationships were analyzed and combined in a set of equations that constitute the core of the model. This set of equations was then encapsulated in the developed software to interface the deposit model with spray gun trajectories issued from robotic simulation software. Such a system permits hence to predict some deposit properties for a given set of processing parameters and for a given trajectory. This paper presents the experimental procedure leading to the determination of the deposit model, a few relationships, the procedure to define optimized simulated trajectories, and the procedure to simulate the deposit manufacturing. Then, some results are provided and discussed in relation to experimental data.

Plasma spraying is a key industrial coating process that exhibits intricate nonlinear interactions among process parameters. This complexity makes accurate predictions of particle properties, which greatly affect process behavior, very challenging. Specifically, particle velocities and temperatures profoundly impact coating quality and process efficiency. Conventional methods often require empirical correlations and extensive parameter tuning due to their limited ability to capture the underlying physics within this intricate system. This study introduces Physics-Informed Neural Networks (PINNs) as a solution. By seamlessly integrating known physical laws and constraints directly into the model architecture, PINNs offer the potential to learn the underlying physics of the system. For comparison, Artificial Neural Networks (ANNs) are also developed. Computational Fluid Dynamics (CFD) simulations of a plasma generator and plasma jet model provide data to train both ANN and PINN models. The study reveals an improvement in particle velocity prediction through the proposed PINN model, demonstrating its capability to handle complex relationships. However, challenges arise in predicting particle temperature, warranting further investigation. The developed models can aid in optimizing the plasma spraying process by predicting essential particle properties and guiding necessary process adjustments to enhance coating quality.

This paper presents the AccurasprayHub, a centralized data platform designed to harmonize booth parameters, in-situ plume measurements, maintenance schedules, and coating quality evaluations. By combining domain expertise with advanced analytics, including initial steps toward machine learning, the AccurasprayHub establishes robust datasets, identifies stable process windows, and provides proactive insights for process improvements.

Cold spray additive manufacturing (CSAM) is an emerging process that has garnered significant attention from researchers due to its unique advantages. These include higher deposition rates, no need for a protective atmosphere, and the ability to connect or combine dissimilar materials. While CSAM allows for near-net-shape fabrication of workpieces, the accuracy and properties of the final products often fall short of user requirements. Furthermore, there is an urgent need to develop a generalized manufacturing strategy for workpieces with complex geometries. It appears that integrating various processes throughout the entire manufacturing workflow, from design to delivery, could address these challenges. However, few researchers have explored this area. To fill this gap, this study presents an integrated modular CSAM system designed for efficient and flexible workpiece fabrication. The system comprises two main components: software for modeling and simulation, and hardware for precise fabrication, each containing multiple modules. These modules do not operate independently but are coupled through direct or indirect decentralized and event-driven physical links. The system described in this paper offers a generalized strategy for precision manufacturing of workpieces using CSAM, potentially advancing the field and addressing current limitations in accuracy and versatility.

One approach for controlling the twin wire arc spray (TWAS) process is to use optical properties of the particle beam like length or brightness of the beam as input parameters for a process control. The idea is that changes in the process like eroded contact nozzles or variations of current, voltage and/or atomizing gas pressure can be detected through observation of optical properties of the particle beam. It can be assumed that if these properties deviate significantly from those obtained from a beam recorded for an optimal coating process the spray particle and so the coating properties change significantly. Thus, the goal is to detect these optical deviations and compensate occurring errors by adjusting appropriate process parameters for the wire arc spray unit. One cost effective method for monitoring optical properties of the particle beam is to apply the process diagnostic system PFI (Particle Flux Imaging): PFI fits an ellipse to an image of a particle beam thereby defining easy to analyze characteristic parameters by relating optical beam properties to ellipse parameters. Using artificial neural networks (ANN) mathematical relations between ellipse and process parameters can be defined. Thus in the case of a process disturbance through the use of an ANN-based control new process parameters can be computed to compensate particle beam deviations. In this paper, it will be shown that different process parameters can lead to particle beams with the same PFI parameters.