Analysis and Optimization of the HVOF Process by Artificial Neural Networks Model
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | Analysis and Optimization of the HVOF Process by Artificial Neural Networks Model |
| Type de publication | Conference Paper |
| Year of Publication | 2018 |
| Auteurs | Meimei L, Zexin Y, Chaoyue C, Hanlin L, Sihao D |
| Editor | Azarmi F, Balani K, Eden T, Hussain T, Lau YC, Li H, Shinoda K, Toma FL, Veilleux J |
| Conference Name | INTERNATIONAL THERMAL SPRAY CONFERENCE AND EXPOSITION (ITSC 2018) |
| Publisher | ASM Int, Thermal Spray Soc; ASM Int; German Welding Soc; IIW |
| Conference Location | 9503 KINSMAN RD, MATERIALS PARK, OH 44073 USA |
| ISBN Number | 978-1-62708-160-3 |
| Mots-clés | ANN model, HA coatings, HVOF, in-flight particles, spraying system |
| Résumé | 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 in-flight 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. |