Loading Rate and Temperature Interaction Effects on the Mode I Fracture Response of a Ductile Polyurethane Adhesive Used in the Automotive Industry.

Due to their high elongation at failure and damping capacity, polyurethanes are one of the main types of adhesives used in automotive structures. However, despite the wide range of applications of adhesives, their fracture mechanics behavior is still poorly studied in the literature, especially when both the loading rate and ambient temperature change. Accordingly, the main aim of the current work is to deal with the research gap. In the current research, mode I fracture energy of a ductile polyurethane adhesive with adaptive properties for its industrial application is determined at different test speeds and temperatures. Tests were done at quasi-static, intermediate, and high-speed levels and each at three different temperatures, including low, high, and room temperature. Mode I fracture toughness was determined using DCB tests. Increasing the loading rate from quasi-static to 6000 mm/min was found to significantly increase the maximum strength of the tested DCBs (from 1770 N to about 4180 N). The greatest sensitivity to the loading rate was observed for the DCBs tested at room temperature, where the fracture energy increased by a factor of 3.5 from quasi-static (0.2 mm/min) to a high loading rate (6000 mm/min). The stiffness analysis of the DCB samples showed that the transition from below the Tg to room temperature decreases the bond stiffness by about 60%, while a further temperature increase (from 23 °C to 60 °C) has no significant effect on this parameter. Since polyurethane-bonded joints often experience a wide range of temperatures and loading rates in service, the obtained results can be used to design these joints more securely against such loading/environmental conditions. [ABSTRACT FROM AUTHOR]

Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)