Enhancing Predictive Quality in HVOF Coating Technology: A Comparative Analysis of Machine Learning Techniques

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This paper presents a comparative analysis of the performance of machine learning techniques for enhancing predictive quality in thermal spray coating technology, with a focus on the high velocity oxygen fuel (HVOF) process. The study aims for accurately predicting coating properties, which are pivotal in determining coating quality in HVOF applications. By investigating five influential factors, including the fuel-to-oxygen ratio and coating velocity, this research was designed to improve the understanding and control of these properties. Through advanced statistical design of experiments, correlations between these factors and key coating properties are established. Six machine learning models are trained and evaluated to assess their predictive capabilities for coating properties. The obtained results provide valuable insights into the HVOF coating process and highlight the potential of predictive model-based techniques for accurately predicting such properties. Furthermore, this research offers guidance for selecting an effective machine learning technique in the context of thermal spray technology.