Polarization Scattering-based Detection of Lubricating Materials

Xin Liu¹Junxin Jian¹Xiaolu Huang¹Huilong Li¹Yuan Tian²Weizhi Ma¹Xiaomlong Zhao¹Jiayi Sun¹Xiushuo Zhang¹Yong Wang^1*Xiaomei Zhu^3*

• ¹Qingdao University of Technology, Qingdao, China
• ²Zaozhuang Vocational College, Zaozhuang, China
• ³Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China

Friction, wear, and corrosion are common phenomena in both industrial and daily life, significantly affecting the performance, lifespan, and energy efficiency of equipment. To mitigate these adverse effects, current research primarily focuses on the development of novel materials, surface modification techniques, and lubricating coatings. Accurate detection and evaluation are crucial for understanding material behavior under different conditions, which not only aids in optimizing material design but also enables timely identification and repair of potential issues, thereby extending equipment lifespan and enhancing reliability. However, existing detection and evaluation methods still have limitations in certain scenarios, particularly in real-time monitoring and micro-defect detection. To address this, this study proposes a polarization-based detection method aimed at more accurately assessing and optimizing the friction, wear, and corrosion properties of materials. Polarization methods leverage the polarization characteristics of light to investigate the optical properties and microstructures of materials. These methods offer advantages such as high data dimensionality, sensitivity to microstructural changes, and compatibility with existing optical techniques. Using our constructed polarization detection system, we measured various samples and employed data analysis methods like linear discriminant analysis to study the differences in polarization scattering characteristics among different lubricating materials. We extracted specific parameters that are sensitive to different characteristic variables. Through these specific parameters, we were able to more accurately characterize the lubrication performance of materials, providing valuable insights for material optimization.