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It is evident that most rubber components in the automotive industry are subjected to repetitive loading. Vigorous research is needed towards improving the safety and reliability of the components. The study is conducted on an automotive rubber jounce bumper with a rubber hardness of 60 IRHD. The test is conducted in displacement controlled environment under compressive load. The existing models by Kim, Harbour, Woo and Li are adopted to predict the fatigue life. The experimental results show strong similarities with the predicted models.

Most rubber components in the automotive industry are subjected to static and dynamic loading. Research on fatigue analysis and ways to enhance fatigue life is constantly done as it’s directly related to the safety and reliability of a product. Fatigue life determination carried out experimentally has the best accuracy however these methods are not feasible when the components are constantly being renewed. The study was done on an automotive rubber jounce bumper with a rubber hardness of 60 IRHD. The test was conducted in displacement controlled environment under compressive load. The compression test reveals that the jounce bumper is able to withstand a maximum force of 7 KN. The fatigue life of the rubber jounce bumper was successfully determined at three zones by separating the safe zone from the potential danger zone. The existing models by Kim, Harbour, Woo and Li were used to compare and validate the fatigue life. The SEM result shows the existence of decohesions in the control jounce bumper.

It is evident that most rubber components in the automotive industry are subjected to repetitive loading. Vigorous research is needed towards improving the safety and reliability of the components. The study was done on an automotive rubber jounce bumper with a rubber hardness of 60 IRHD. The test was conducted in displacement-controlled environment under compressive load. The existing models by Kim, Harbour, Woo and Li were adopted to predict the fatigue life. The experimental results show strong similarities with the predicted models.