A New Multiparameter Model for Multiaxial Fatigue Life Prediction of Rubber Materials

Area of Science:

• Materials Science
• Mechanical Engineering
• Polymer Science

Background:

• Mechanical components made from rubber materials are susceptible to fatigue failure under fluctuating loads, significantly limiting their service life.
• Existing fatigue life prediction models for rubber are often material-specific and specimen-dependent, lacking generalizability.
• Accurate fatigue life prediction is crucial for enhancing the durability and reliability of rubber components in various applications.

Purpose of the Study:

• To develop a novel, generalized multiaxial fatigue model for rubber materials.
• To enhance the accuracy of fatigue life prediction by incorporating multiple critical parameters.
• To provide a more versatile tool for analyzing elastomer fatigue under diverse experimental conditions.

Main Methods:

• Development of a new generalized multiaxial fatigue model incorporating a multiparameter variable.
• Simultaneous consideration of stresses, strains, and strain energies for improved prediction.
• Verification of the model through correlation with published fatigue test data for various rubber materials.
• Comparative analysis against over 20 existing parameters using the R² coefficient.

Main Results:

• The proposed model demonstrates a significant improvement in predicting the fatigue life of rubber materials.
• The multiparameter approach effectively captures complex fatigue behaviors under multiaxial loading.
• The model shows superior performance compared to more than 20 previously used parameters in the literature.
• High R² values were obtained when comparing predicted fatigue lives with experimental data.

Conclusions:

• The developed generalized multiaxial fatigue model offers a reliable and improved approach for elastomer fatigue life prediction.
• This model provides a valuable tool for processing experimental fatigue data obtained under varied conditions.
• While not a universal solution, it represents a significant advancement in understanding and predicting rubber material fatigue.