The influence of air plasma sprayed alumina coating geometry, microstructure, interface roughness on its delamination and crack propagation resistance during low temperature thermal cycling, i.e. thermal mismatch stress, is investigated both numerically and experimentally. Previous studies on thermal cycling loading concentrate on flat, numerically designed locally curved specimens and/or mathematically modeled roughness without extension towards real coating morphology, which renders the conclusions less practically driven. Results show that arbitrarily oriented cracks originate predominantly near the coating/substrate interface and propagate along zones of high tensile and shear residual stress. The crack path deflection was attributed to the complex stress concentration structure resultant from the intricate microstructural porosity and coating general convex geometry. Microstructural features such as porosity increase the interfacial and coating tensile stress, which may lead to important delamination processes even during low temperature thermal cycling.

The use of process-microstructure-property relationships for cold spray can significantly reduce application development cost and time compared to legacy trial and error strategies. However, due to the heterogeneous microstructure of a cold spray deposit, with (prior) particle boundaries outlining consolidated splats (deformed particles) in the as-spray condition, the use of automated analysis methods is challenging. In this work, we demonstrate the utility of quantitative data developed from a convolutional neural network (CNN) for feature extraction of cold spray microstructures. Specifically, the power of CNN is harnessed to automatically segment the deformed particles, which is hardly accessible at scale with traditional image processing techniques. Deposits produced with various processing conditions are evaluated with metallography. Parameters related to particle morphology such as compactness are also quantified and correlated to strength.

Cold spray additive manufacturing is an emerging solid-state deposition process that enables large-scale components to be manufactured at high production rates. Control over geometry is important for reducing the development and growth of defects during the 3D build process and improving the final dimensional accuracy and quality of components. To this end, a machine learning approach has recently gained interest in modelling additively manufactured geometry; however, such a data-driven modelling framework lacks the explicit consideration of a depositing surface and domain knowledge in cold spray additive manufacturing. Therefore, this study presents surface-aware data-driven modelling of an overlapping-track profile using a Gaussian Process Regression model. The proposed Gaussian Process modelling framework explicitly incorporated two relevant geometric features (i.e., surface type and polar length from the nozzle exit to the surface) and a widely adopted Gaussian superposing model as prior domain knowledge in the form of an explicit mean function. It was shown that the proposed model is able to provide better predictive performance than the Gaussian superposing model alone and purely data-driven Gaussian Process model, providing consistent overlapping-track profile predictions at all overlapping ratios. By combining accurate prediction of track geometry with toolpath planning, it is anticipated that improved geometric control and product quality can be achieved in cold spray additive manufacturing.

In this work, we propose a novel visual navigation method to estimate the state of mobile and fixed cold-spray material deposition systems using a stereo-camera sensor installed in the workspace. Unlike other visual localization algorithms that exploit costly onboard sensors such as LiDARs or fully rely on distinct visual cues on the robot and grid markers in the environment, our method significantly reduces the cost and complexity of the sensory setup by utilizing a cost-effective remote stereo vision system. This allows for the localization of the target system regardless of its appearance or the environment, and enables scalability for tracking and operation of multiple mobile material deposition systems at the same time. To achieve this aim, deep neural networks, kinematic constraints, and learning-aided state observers are employed to detect and estimate the location and orientation of the deposition system. A physical model of the system with bounded uncertainty and fusion with a remote visual sensing module is proposed. This accounts for frames in which depth estimation accuracy is reduced due to perceptually degraded conditions in the cold spraying context. The algorithm is evaluated on a fixed and mobile setup that demonstrate the accuracy and reliability of the proposed method.

The Cold spray (CS) is a promising solid-state additive manufacturing method. The interesting physics involved in the CS process including cold, high strain rate, adiabatic and severe plastic deformation results in a unique and complex structure of CS deposits at different length scales that directly determines the properties of the deposits. Therefore process- structure properties (performance) (PSP) linkages explorations are pivotal. Integrated computational materials engineering (ICME) methods in complement with experimental analyses are required to evaluate materials properties and behaviour in PSP links exploration. Finite element modelling is used to simulate the thermomechanical response of materials and evolution of field variables in CS, i.e stress, strain, strain rate, and temperature, at structural scales. Molecular dynamics modellings of nano-particle impact have provided useful insights into atomic-scale phenomena of individual particle impact while the modelling of microstructure evolution in micro and mesoscale has yet to be investigated. In this study, we developed and implemented a thermodynamic phase field simulation method to capture the structure evolution of CS composite Ni-Ti deposit upon post-spray heat treatment (PSHT) in microstructure scale. The external or internal stimuli such as heat and strain either generated in the system because of phase transformation or stored as internal energy upon CS process are accounted for. The interface mobility and microstructure development are calculated by minimization of Gibbs free energy of the system. The comparison of the simulated microstructure with experimental results confirms that the phase field modelling precisely predicts the microstructure evolution of the CS deposits upon PSHT.

A variety of process parameters affect the properties of the deposited coatings in the High Velocity Oxygen Fuel (HVOF) spraying process. In fact, the quality of coatings can be improved without changing feedstock or deposition technology by the application of optimized spraying process parameters. In this study, a large set of data “Big Data” is used to create a variety of machine learning models for prediction of porosity content and hardness values of HVOF deposited coatings. A set of process parameters was selected as validation run and actual HVOF coating was deposited using those parameters. The porosity level and hardness were measured and compared to those predicted by models. The models differ based on the number of neurons utilized in each layer for the calculations. A model with six neurons could predict closest porosity level and the one with three was the best in prediction of hardness. The final model could be obtained by running data through both models. Through this study, a robust machine learning model for the optimization of HVOF process parameters will be developed that could be used for other coatings and thermal spraying techniques.