Prediction of micro wear depth between engineering polymers

• Published in: Journal of manufacturing processes Vol. 124; pp. 1124 - 1134
• Main Authors: Kim, Heuisoo, Kang, Seongye, Han, Insik, Eun, Youngjin, Nam, Junyoung, Kim, Dongchoul
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 30.08.2024
• Subjects: Micro wear; Polymer; Wear coefficient; Micro wear; Polymer; Wear coefficient
• ISSN: 1526-6125; 2212-4616
• DOI: 10.1016/j.jmapro.2024.07.024

In the polymer industry, changes in the wear area that affect surface aesthetics have emerged as a significant issue in applications where surface appearance is crucial. Even minute changes in surface height can be visible to the naked eye, necessitating the need for micro-scale wear prediction of polymers. While the Archard model has been verified for decades for metal-metal or polymer-metal contact, predicting wear between polymer-polymer contacts remains challenging due to limitations in wear measurement and wear coefficient determination. In this study, we propose a method for determining the wear coefficient for polymer-polymer contact and validate the prediction model with a component wear test on polymer parts. Wear depth, an essential indicator of wear is measured after the sliding wear test of the polycarbonate (PC) pin and Acrylonitrile butadiene styrene (ABS) copolymer plate. The wear prediction model with the obtained wear coefficient exhibits 99.7 % accuracy in comparison to the test wear depth. A component wear test is performed on engineering polymer parts, and wear prediction under test conditions is made using the proposed model. The measured and predicted wear depth of the component demonstrates a difference smaller than 5 μm, which is within the range of no change in scratch visibility. This study ensures the accurate wear prediction of engineering polymer components with affordable computational efficiency. The ability to predict polymer-polymer wear, previously unattainable, will enable several applications in the polymer industry, such as component design, scratch visibility prediction, and quality assurance.