Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

This study aims to employ a machine learning (ML)‐based regression model that accurately captures nonlinear relationships between electroplating process parameters and chromium thickness, while enabling the interpretation and visualization of these nonlinear effects. For this purpose, two statistically distinct datasets from laboratory‐scale (1L) and pilot‐scale (14L) experiments were analyzed. Hyperparameter tuning and fivefold cross‐validation are used for training to make the results robust and transparent for different data constellations. Several models were evaluated and achieved coefficients of determination () of up to 75%, often outperforming linear regression (LR) due to nonlinear parameter interactions. Model performance varied depending on the dataset, with no single ML approach proving universally superior. For the entire dataset, the CatBoost model achieved the best result with an average of 70.5%. A deeper analysis of the data was performed using SHAP, permutation importance, and global feature importance. To visualize potential nonlinear relationships, a partial dependence analysis was conducted to assess the influence of individual process parameters. This analysis confirmed the presence of specific nonlinear dependencies for several parameters in relation to chromium thickness. The correlations among the process parameters identified in this study are highly relevant for industrial bath management and process optimization.