3-D Particle Surface Reconstruction From Multiview 2-D Images With Structure From Motion and Shape From Shading (January 2020)

Since wear particles usually contain valuable information about lubricant and wear condition of lubricated components, wear debris analysis is widely used to monitor machine/component wear. Current literature reports about the importance and necessity of 3-D surface characterization of wear particles as opposed to 2-D characterization. It may be borne in mind that traditional 3-D analysis has been a synonym only for local surface morphologies for stationary particles. This results in large errors when irregular particles are under observation. A new 3-D imaging methodology is proposed to reconstruct multiview 3-D surfaces for a moving particle whereby the target particle is passed through a microchannel with rolling motion, and recorded via a digital microscope. The multiple moving particles are tracked based on Kalman filter to obtain different-view images. Sparse and dense reconstruction of the particle surfaces are enabled by these images using Structure from Motion (SfM) and Shape from Shading (SfS) methodologies, respectively. The method was verified using real particles and evaluated by the results of laser scanning confocal microscopy. The proposed method is able to reconstruct wear particles under various perspectives, and the errors from the characterization parameters of the areal are less than 20%.

I. Introduction

Valuable information about a wear process, including the wear severity and wear mechanism, can be revealed through wear debris analysis (WDA). On account of this notion, WDA has been adopted over the years as the primary candidate for wear diagnosis [1], [2]. However, this technique may provide marginal results by virtue of its empirical dependence on the identification of wear particles. Based on increasing demands for an accurate, reliable, and efficient analysis process, there is an increasing emergence in the WDA by shifting from 2-D to 3-D analysis with the objective of acquiring more comprehensive and accurate information of particle morphologies. As significant 3-D information, the surfaces have recorded their generation mechanism with obvious features, such as scratches on the surface of severe sliding particles and pits on the surface of fatigue particles. Therefore, further development of new methods for extracting comprehensive features is important for wear particle identification, which is critical for wear mechanism assessment.