Porosity prediction of cold sprayed titanium parts using machine learning

Abstract

The desired porosity level of cold-sprayed titanium parts varies depending on the application and therefore requires precise control. To achieve the desired porosity the selection of the correct spray parameters is essential. This study investigates how the cold spraying process affects porosity levels through the application of machine learning techniques. 14 parameters are recorded during the cold spraying process of titanium parts, with the porosity level of each process being manually measured through the analysis of microscope images. Due to the high cost associated with generating data, the dataset size was limited for this study. To alleviate this problem such that machine learning models can be properly trained, this paper carefully enhances a firsthand dataset by using feature engineering, feature selection, and dimension reduction techniques. The study implemented random forest, gradient boosting, and neural network algorithms, with the neural network model demonstrating the best performance. This model achieved an RMSE of 0.7 % on unseen data. For the spray parameter ranges of the available dataset, based on the Shapley value analysis, the spray angle has been identified as the most influential feature of the model for predicting porosity.