High-velocity oxygen fuel (HVOF) spraying is a promising technique for depositing protective coatings. The performances of HVOF-sprayed coatings are affected by in-flight particle properties, such as temperature and velocity, that are controlled by the spraying parameters. However, obtaining the desired coatings through experimental methods alone is challenging, owing to the complex physical and chemical processes involved in the HVOF approach. Compared with traditional experimental methods, a novel method for optimizing and predicting coating performance is presented herein; this method involves combining machine learning techniques with thermal spray technology. Herein, we firstly introduce physics-informed neural networks (PINNs) and convolutional neural networks (CNNs) to address the overfitting problem in small-sample algorithms and then apply the algorithms to HVOF processes and HVOF-sprayed coatings. We proposed the PINN and CNN hierarchical neural network to establish prediction models for the in-flight particle properties and performances of NiCr–Cr₃C₂ coatings (e.g., porosity, microhardness, and wear rate). Additionally, a random forest model is used to evaluate the relative importance of the effect of the spraying parameters on the properties of in-flight particles and coating performance. We find that the particle temperature and velocity as well as the coating performances (porosity, wear resistance, and microhardness) can be predicted with up to 99% accuracy and that the spraying distance and velocity of in-flight particles exert the most substantial effects on the in-flight particle properties and coating performance, respectively. This study can serve as a theoretical reference for the development of intelligent HVOF systems in the future.

Binary spinel-type metal oxides (AB ₂ O ₄ ) related materials, including ferrites (AFe ₂ O ₄ ), are attractive photocatalysts thanks to their excellent visible light response for the photodegradation of organic pollutants. Currently, these materials are synthesized via conventional chemical routes suffering from long preparation duration and multistep. Moreover, the photocatalysts are obtained as nano-powders from conventional chemical routes would introduce another drawback for their recycling and reuse. From an industrial perspective, it is desirable to develop an efficient and facile synthesis process to produce photocatalysts in a non-dispersible form. Herein, we demonstrate that the solution precursor plasma spray (SPPS) process is a single-step method for depositing photocatalytically active zinc ferrite-based films within several minutes. The influence of the precursor ratio on the microstructures and phase compositions of the ZnFe ₂ O ₄ films was investigated by XRD and Raman analyses. In addition, two optimized ZnFe ₂ O ₄ films were prepared by increasing the ZnO loading and tailoring injection pattern of the precursor solution. The surface morphologies and optical bandgap were also determined by SEM and UV–visible spectroscopy. The photocatalytic activities of the ZnFe ₂ O ₄ films were evaluated through the degradation of the Orange II dye and of tetracycline hydrochloride under UV or visible light irradiation. The results show that compositional ratios and composition distribution of the ZnFe ₂ O ₄ films prepared via SPPS played a key role on the photocatalytic activity. The SPPS route was demonstrated to be a promising method for the synthesis and the deposition of metal oxide (i.e. perovskite type and spinel type) films within a single-step for functional applications.