Surface Topography Characterization Using a Simple Optical Device and Artificial Neural Networks

Christoph Angermann; Markus Haltmeier; Christian Laubichler; Steinbjörn Jónsson; Matthias Schwab; Adéla Moravová; Constantin Kiesling; Martin Kober; Wolfgang Fimml

arXiv · 2103.08482

Surface Topography Characterization Using a Simple Optical Device and Artificial Neural Networks

Christoph Angermann·Markus Haltmeier·Christian Laubichler·Steinbjörn Jónsson·Matthias Schwab·Adéla Moravová·Constantin Kiesling·Martin Kober·Wolfgang Fimml

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Abstract

State-of-the-art methods for quantifying wear in cylinder liners of large internal combustion engines require disassembly and cutting of the liner. This is followed by laboratory-based high-resolution microscopic surface depth measurement that quantitatively evaluates wear based on bearing load curves (Abbott-Firestone curves). Such methods are destructive, time-consuming and costly. The goal of the research presented is to develop nondestructive yet reliable methods for quantifying the surface topography. A novel machine learning framework is proposed that allows prediction of the bearing load curves from RGB images of the liner surface that can be collected with a handheld microscope. A joint deep learning approach involving two neural network modules optimizes the prediction quality of surface roughness parameters as well and is trained using a custom-built database containing 422 aligned depth profile and reflection image pairs of liner surfaces. The observed success suggests its great potential for on-site wear assessment of engines during service.

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Christoph Angermann, Markus Haltmeier, Christian Laubichler, Steinbjörn Jónsson, Matthias Schwab, Adéla Moravová, Constantin Kiesling, Martin Kober, Wolfgang Fimml. 2021-03-15. Surface Topography Characterization Using a Simple Optical Device and Artificial Neural Networks. https://arxiv.org/abs/2103.08482

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