Manufacturing tolerances pose considerable challenges in the production of rubber-based components, often leading to dimensional variations that can compromise overall product reliability. These inconsistencies may result in a range of functional issues, including poor sealing, excessive wear, misfit during assembly, and premature failure. Among these components, grommets are especially sensitive due to the complex, non-linear behavior of rubber materials, making precise dimensional control critical to ensure consistent and dependable performance.
Even minor deviations in grommet geometry can trigger substantial assembly difficulties. Issues such as misalignment, buckling, insertion challenges, and potential damage to neighboring parts are commonly encountered. These complications typically require multiple rounds of design revisions and physical testing, which extend development timelines and significantly increase production costs. Real-world observations from various automotive applications underscore the practical impact of these manufacturing-induced problems.
To proactively address these issues, this study introduces a digital methodology aimed at evaluating design robustness in the early stages of product development. The primary goal is to predict performance variability under a wide range of operating conditions, thus reducing the reliance on physical prototypes. While buckling is the primary failure mode investigated, other functional concerns are also taken into consideration.
The research specifically focuses on understanding how key geometric parameters—such as overall length, feature pitch, and convolute height—influence grommet behavior. A stochastic analysis is used to simulate variations in these parameters across different combinations, allowing for a comprehensive evaluation of their impact on both functionality and assembly robustness.
The insights gained from this data-driven approach aim to improve reliability, optimize assembly processes, and lower development costs. Ultimately, the methodology supports smarter design decisions, enabling more robust and efficient product development.