Study Of Surface Characteristics Both Of Wear Particles & Wear Components And Their Relationship In Wear Process

The recognition of wear condition as well as wear mechanism in wear process depends on the analysis of surface topographies of wear components and wear debris generated in the wear process. For a machine in operation, it is very convenient to collect wear particles generated in wear process for conducting wear debris analysis. However, it is definitely difficult to access to study the surface of wear components. Therefore, it is very crucial and significant to study the relationship between the surface of wear components and the surface of wear particles.This paper has proposed a set of methods and techniques to acquire appropriate images using confocal laser scanning microscopy, to separate roughness, waviness, and form using wavelet theory, and to calculate 2D & 3D surface parameters of wear particles and engineering surfaces for quantifying surface characterization of engineering surfaces and surfaces of small particles using computerized image analysis techniques. The use of the approaches for 3D surface parameters developed in this paper will play a great role in studying surfaces of wear particles and surfaces of wear components. Benefited from the above approaches, this project focused on the sliding wear mode in mining and port machinery and conducted a series of wear tests to study the effects of contaminants, temperature and lubricant condition on wear process and the surface evolutions of wear particles and wear components as wear progresses.For all mining and port machinery, their lubricants are very likely to be polluted by contaminants such as silica and other metallic debris such as iron and nickel. In order to seek a deeper understanding of the effects of different contaminants on wear process, this project conducted sliding wear processes using pin-on-disc tester when silica powder and iron powder exist in N32 lubricant to study the surface characterization of wear particles and wear components in different wear stages. The independent and dependent role of silica and iron powder in sliding wear processes was also analyzed based on experimental results. Furthermore, sliding wear tests have been conducted to investigate the effects of temperature on sliding wear processes when the iron particle contaminants were existent or non-existent in the SAE40lubricant. The experimental results clearly showed that the temperature of the S AE40 lubricating oil has a significant influence on the wear processes. The increase in the temperature of the SAE40 lubricant increases the probability and the degree of adhesion and oxidation. It is very clear that containments and lubricant temperature did play an important role in wear process. It is believed that the knowledge gained in this study is significant for a deeper understanding of the wear mechanisms and wear characteristics as well as for controlling wear rates.Surface characterization, particularly roughness analysis, is very important for a wide range of applications involved with the control of friction, lubrication, and wear. Sliding wear tests were conducted on the tester at room temperature to investigate the surface roughness evolutions of both wear components and the wear particles generated from tests as wear progresses under proper lubrication and improper lubrication conditions. The results showed that there is good correlation of the surface morphology of wear particles and the surface morphology of the wear components. In order to seek a further correlation of surfaces of wear particles and surfaces of wear components, more surface parameters were adopted to describe the surface characteristics. Subsequently, information fusion technology was used to investigate the mapping relationship between surface topographies of wear particles and those of corresponding wear components. Firstly, appropriate parameters of wear particles are selected to recognize types of wear particles using neural network to determine the main representative types of wear particles generated in the wear process. Then, the surface characterization of these representative wear particles was gained to conduct the relationship between surface characteristics of wear particles and those of wear components. The demonstrated case has showed that the surface characteristics of wear components can be assessed through studying the surface characteristics of the wear particles. It is believed that the surface characteristics of wear components can be predicted according to the surface characteristics of wear particles, which will contribute to machine condition monitoring.