Analysis of Data Generation and Preparation for Porosity Prediction in Cold Spray using Machine Learning

Abstract

AbstractCold spray is an additive manufacturing and coating process in which powder particles are accelerated to supersonic speeds without melting them and then deposit on a surface to form a layer of a coating. Process parameters and materials affect the characteristics of manufactured parts and therefore must be chosen with care. Machine learning (ML) techniques have been specifically applied in additive manufacturing for tasks such as predicting and characterizing porosity. Machine learning algorithms can learn how a variation in the input spray parameters affects annotated output data, such as experimentally measured part properties. In this work, a dataset was developed from experiments reported in published academic papers, to train ML algorithms for the porosity prediction of cold spray manufactured parts. Data cleaning steps, such as null value replacement and categorical feature handling, were applied to prepare the dataset for the training of different ML models. The dataset was split into training and testing portions, and floating feature selection and hyperparameter optimization were performed using parts of the training set. A final evaluation of all trained models, using the test portion of the dataset, showed that a prediction accuracy with an average deviation of 0-2% porosity of the predicted values compared to the true values can be achieved. Graphical Abstract