Prediction of Quality in Thermographic Spray Spot Applications with Machine Learning Algorithms

Due to the high number of influences in production applications, quality fluctuation and deviating results in thermal spray applications are a challenging issue in manufacturing processes. This paper presents a concept for an adaptive process control based on different machine learning algorithms that are utilized to correlate input parameters with output results in thermal spray processes. For that purpose, a test environment was developed, incorporating different application scenarios with various input parameters. During the experiments, a series of measurements was performed, that is used to describe and evaluate the process. The extracted data of the conducted experiments are transformed into features and serve as the data input for the development of machine learning models based on various algorithms. These algorithms offer a prediction of the peak position, which describes the deviation of the programmed application spot, thus offering a quality prediction by utilizing solely the input process parameters. This work provides an approach based on the compared algorithms, that can be applied to create an adaptive process control, by adjusting the input parameters during the thermal spray application process.