Reshaping Thermal Spraying: Explainable Artificial Intelligence Meets Plasma Spraying

Thermal spray, vol. 85045, pp. 237–244

Abstract

Abstract This study employs an XAI framework to gain insights into Residual Network and Artificial Neural Network models trained on both simulations and experimental data to predict deposition efficiency (DE) in atmospheric plasma spraying (APS). SHapley Additive exPlanations (SHAP), an interpretability framework, was then applied to help identify which process parameters have the most significant influence on the DE and to reveal how changes in specific parameters affect the DE by elucidating their impact on the model predictions.

Authors 4

1. Kirsten Bobzin Aachen Surface Engineering Institute (IOT)

   RWTH Aachen University

   Affiliation as printed

   Surface Engineering Institute, RWTH Aachen University , Aachen, Germany

2. H. Heinemann Aachen Surface Engineering Institute (IOT)

   RWTH Aachen University

   Affiliation as printed

   Surface Engineering Institute, RWTH Aachen University , Aachen, Germany

3. M. Erck Aachen Surface Engineering Institute (IOT)

   RWTH Aachen University

   Affiliation as printed

   Surface Engineering Institute, RWTH Aachen University , Aachen, Germany

4. G. Nassar Aachen Surface Engineering Institute (IOT)

   RWTH Aachen University

   Affiliation as printed

   Surface Engineering Institute, RWTH Aachen University , Aachen, Germany