Enhancing O-Ring Performance Prediction Using Machine Learning Tata Consultancy Services

Format:

Book

Conference/Event

Author/Creator:

Mallu, Venkata Reddy, author.

Contributor:

Chirravuri, Bhaskara

Duddu, Varaprasad

Miller, Ronald

Penumatsa, Venkata Ramana Raju

Sahu, Abhishek

Conference Name:

Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility (ADMMS'25) (2025-02-07 : Chennai, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

O-rings are essential components in engineering products as they ensure leak-proof sealing and hinders amalgamation of various fluids in the system. O-rings in general have lot of factors that go into deciding the right design for a system. With the help of FEA, O-ring design is varied to ensure optimal results. However, this process is time and resource consuming. Considering this situation, an alternative approach to predict the outcome with the help of DOE study is chosen in this paper. It leverages the Machine Learning models to predict the output parameters effectively with less resources. With the help of performance parameters, this paper proposes a comparison of various native ML models like Linear Regression, Random Forrest, SVM, KNN, Boosting, Artificial Neural Networks and Kriging [7]. The Goal is to systematically compare the prediction performance of various models based on bootstrapping and hypothesis testing techniques to identify the most effective approach. This research aims to enhance the efficiency and reliability of predictive modelling and help in virtual validation of O-rings

Notes:

Vendor supplied data

Publisher Number:

2025-28-0161

Access Restriction:

Restricted for use by site license