Design of Amorphous Carbon Coatings Using Gaussian Processes and Advanced Data Visualization

In recent years, an increasing number of machine learning applications in tribology and coating design have been reported. Motivated by this, this contribution highlights the use Gaussian processes for prediction resulting characteristics to enhance amorphous carbon coatings. regard, using process regression (GPR) models, a visualization map available is created. The training GPR models based on experimental results centrally composed full factorial 23 deposition a-C:H coatings medical UHMWPE. addition, different supervised (ML) such as Polynomial Regression (PR), Support Vector Machines (SVM) Neural Networks (NN) are trained. All then used predict indentation hardness complete statistical Box–Behnken design. finally compared, with being superior performance. performance overall approach, terms quality quantity predictions well usage visualization, demonstrated initial dataset 10 characterized