Feasibility Study of Combining Data from Different Sources Within Artificial Intelligence Models to Reduce the Need for Constant Velocity Joint Test Rig Runs

Within this paper, the feasibility of reducing test rig runs in constant velocity joint (CVJ) development by combining data from different sources (simulation and test rig) for artificial intelligence (AI) models has been investigated. Therefore, a case study on CVJ efficiency prediction using a random forest regressor, a decision-tree-based algorithm, was conducted using a data set of 95,798 points derived from both test rigs (52,486 points) and multi-body simulations (43,312 points). The amount of test rig data in the training data set of the regression model was iteratively reduced from 100% to 12.5% to investigate the need of expensive test rig data. Additionally, clustering models related to KMeans-algorithm were performed, to achieve further improvements of the AI models and more information about the data. Furthermore, regression and clustering models were performed with data dimensionally reduced by principal component analysis (PCA) to improve model complexity and performance. The number of principal components for the regression model was reduced from 65 to 5 components to investigate their influence on the models predictions. The study showed that combining data from different sources has a positive impact on the predictions of AI models and the confidence of their results, even though the R2-Score of the trained regression models did not change significantly, ranging from 0.927% to 0.9497%.