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

TL;DR

This paper introduces a non-destructive, machine learning-based method to quantify surface wear in engine liners using RGB images and neural networks, enabling on-site assessment.

Contribution

It presents a novel deep learning framework that predicts bearing load curves from surface images, reducing reliance on destructive laboratory methods.

Findings

High prediction accuracy of surface roughness parameters

Effective use of a custom database with 422 image-depth pairs

Potential for real-time, on-site engine wear assessment

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.