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Wear Particle Quantitative Classification from Ferrogram Micrographic Images
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DOI：10.1007/s11668-025-02294-5.png)
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Abstract
En 中文
The smooth operation of a machine's parts will determine how healthy it is. Some components may eventually wear out as a result of unavoidable circumstances over time such as crankshafts, gears, spindles and connecting rods. This study focuses on condition-based maintenance technique, used oil ferrographic analysis. By identifying the wear particle characteristics in this way, the failure rate of AISI 1045 components can be reduced. The wear severity of worn-out parts can be determined from the ferrographic analysis, and preventive action can be planned accordingly to improve machine life. In this study, 27 experiments were carried out by following Taguchi's L27 Orthogonal array under ambient temperature condition for sliding pairs with the help of pin-on-disk tribometer. Oil samples collected from the experiments were analyzed with ferrography with the help of direct reading (DR) ferrograph, dual slide ferrogram maker and bichromatic optical microscope. Both large and small wear debris are detected by the DR ferrograph. Ferrogram slides and the Olympus optical microscope's stream basic software were used to gather data on wear parameters such as area, perimeter, mean diameter, shape factor and aspect ratio. Analysis and classification were carried out using the comprehensive data regarding the mean diameter, area and perimeter of wear debris particles. The micrographs were used to identify wear modes such as oxidation, corrosion, adhesion and abrasion.
Keywords:
Wear
Condition monitoring
Wear particle analysis (WPA)
Ferrographic oil analysis
Optical microscopy
Micrpgraphs
Journal
J
IF:
1.2
Papers:
159
Citations:
2.3K