Enhancing O-Ring Performance Prediction Using Machine Learning

O-rings are essential sealants in engineering applications, ensuring leak-tight seals, and preventing fluid leakage. Specifically, in Fuel Cells, they play a crucial role in maintaining system integrity by preventing working fluid leakages. Finite Element Analysis (FEA) is commonly employed for virtual assessments of sealant assemblies. By simulating the behavior of O-rings under various conditions, engineers can evaluate their performance. However, the traditional approach of varying individual input parameters to understand their impact on performance is time-consuming and extensive. To address this challenge, machine learning (ML) algorithms offer an efficient alternative to repetitive traditional FEA. By leveraging ML models, engineers can predict output parameters more effectively, streamlining the analysis process and enhancing accuracy. Despite the benefits of ML, different predictive models exhibit varying prediction performance, leading to inconsistency. Therefore, it is critical to compare these models and determine the most efficient one(s). In this context, this paper proposes a statistical performance comparison methodology based on bootstrapping and hypothesis testing techniques. The goal is to systematically compare the prediction performance of various models and identify the most effective approach. Overall, this research aims to enhance the efficiency and reliability of predictive modeling by rigorously comparing different approaches.