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.

In today’s increasingly competitive global marketplace, thermal spray processes are needed to support higher-value user manufacturing techniques and to satisfy continuously changing customer demands. These require thermal spray systems that are flexible enough to be produced at different scales of manufacturing batches, while still achieving high-quality coatings at a low cost. Therefore, the established and standard thermal spray processes are in the need to be upgraded to catch up with the stringent requirements of Industry 4.0. The purpose of this study is to provide a comprehensive review on the technologies of Industry 4.0 already integrated with thermal spray processes to uplift their applications through advanced digitization and automation, leading to enhanced efficiency and sustainability. To this purpose, the main current challenges facing the thermal spray family are described. Specific technologies including process control for improving the stability of the spray system, characterization for tailoring coatings to the specific requirements of various applications, modeling and simulation for enhanced replicability, analysis and optimization of the process, in situ observation for monitoring and data collection, artificial intelligence for allowing efficient and effective control and decision making and finally robotics for realizing hybrid thermal spray processes are detailed. These progressive technological aspects can play an irreplaceable role in enhancing the quality and applicability of thermal spray processes. Ultimately, the potential for incorporating these advanced technologies into thermal spray process chains is comprehensively evaluated, and the prospective trajectory of thermal spray technology’s evolution is discussed.

Aluminum target material is an important target material and is widely used in preparations of semiconductor films, integrated circuits, display circuits, protective films, decorative films, etc. In this study, pure aluminum coatings were deposited on stainless steel substrates by cold-spray technology as part of an overall project to produce large-size pure aluminum sputtering target materials. The results show that pure aluminum coatings exhibit high adhesive strength (~98 MPa), high deposition efficiency (~95%), and low porosity (~0.3%) on stainless steel substrates. The bonding mechanisms of pure aluminum coatings on stainless steel are a combination of metallurgical and mechanical interlocking. The evolutions of microstructure and mechanical properties of pure aluminum coatings under different heat treatments were also studied. With the increase of heat treatment temperature, it is found that cold-sprayed aluminum coatings become more homogenous in microstructure, the microhardness is reduced, and the adhesive strength seems to be slightly reduced. Overall, this study demonstrates significant advantages of cold-spray technology in depositing high-performance pure aluminum coatings on stainless steels.

Copper and its alloys are widely used in marine environments due to their excellent corrosion resistance and thermal conductivity. Cold spray technology can avoid the thermal damage to the underlying material and is suitable for the manufacturing and repair of parts. In this study, Cu coatings were prepared on 304 stainless steel substrates by high-pressure cold spray technology, and the effects of cold spray parameters on the microstructure, mechanical properties, and cavitation resistance were investigated. The coatings (Cu-N₂1, Cu-N₂2, and Cu-He) were prepared using distinct cold spray parameters: Cu-N₂1 and Cu-N₂2 employed nitrogen gas at 5 MPa/800 °C with different nozzle geometries, while Cu-He utilized helium gas at 3 MPa/600 °C. The results show that the porosity of the Cu coating prepared by cold spray technology is less than 0.1%. The coating treated with helium gas exhibits a higher bonding strength (81.3 MPa), whereas the coating treated with nitrogen demonstrates greater strain hardening (130–136 HV_0.1). XRD results show that no phase change or oxidation occurred for coatings under all cold spraying conditions. After the cavitation test, the mass loss of the Cu coating is significantly less than that of the as-cast copper. The Cu coating surface first develops holes, and with the increase in cavitation time, the hole area begins to increase. However, with prolonged cavitation exposure, the surface of as-cast copper has a large area of holes, and with the increase in cavitation time, the hole growth rate is faster. These observations indicate the cavitation resistance of the Cu coating prepared by cold spray is more than 10 times higher than that of the as-cast copper. This study highlights the potential application of cold spray technology in the preparation of high-performance anti-cavitation copper coatings.