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

Sauer, Christopher; Rothammer, Benedict; Pottin, Nicolai; Bartz, Marcel; Schleich, Benjamin; Wartzack, Sandro

doi:10.3390/lubricants10020022

Open AccessArticle

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

by

Christopher Sauer

^*

,

Benedict Rothammer

,

Nicolai Pottin

,

Marcel Bartz

,

Benjamin Schleich

and

Sandro Wartzack

Engineering Design, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstraße 9, 91058 Erlangen, Germany

^*

Author to whom correspondence should be addressed.

Lubricants 2022, 10(2), 22; https://doi.org/10.3390/lubricants10020022

Submission received: 14 December 2021 / Revised: 27 January 2022 / Accepted: 3 February 2022 / Published: 7 February 2022

(This article belongs to the Special Issue Machine Learning in Tribology)

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Abstract

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

Keywords: machine learning; amorphous carbon coatings; UHWMPE; total knee replacement; Gaussian processes

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MDPI and ACS Style

Sauer, C.; Rothammer, B.; Pottin, N.; Bartz, M.; Schleich, B.; Wartzack, S. Design of Amorphous Carbon Coatings Using Gaussian Processes and Advanced Data Visualization. Lubricants 2022, 10, 22. https://doi.org/10.3390/lubricants10020022

AMA Style

Sauer C, Rothammer B, Pottin N, Bartz M, Schleich B, Wartzack S. Design of Amorphous Carbon Coatings Using Gaussian Processes and Advanced Data Visualization. Lubricants. 2022; 10(2):22. https://doi.org/10.3390/lubricants10020022

Chicago/Turabian Style

Sauer, Christopher, Benedict Rothammer, Nicolai Pottin, Marcel Bartz, Benjamin Schleich, and Sandro Wartzack. 2022. "Design of Amorphous Carbon Coatings Using Gaussian Processes and Advanced Data Visualization" Lubricants 10, no. 2: 22. https://doi.org/10.3390/lubricants10020022

APA Style

Sauer, C., Rothammer, B., Pottin, N., Bartz, M., Schleich, B., & Wartzack, S. (2022). Design of Amorphous Carbon Coatings Using Gaussian Processes and Advanced Data Visualization. Lubricants, 10(2), 22. https://doi.org/10.3390/lubricants10020022

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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

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