Application of AI for Predicting Test Cycles of Drivetrain Component Dana India Technical Center

Format:

Book

Conference/Event

Author/Creator:

Sirsaj, author.

Contributor:

Kharche

Conference Name:

The 26th Small Powertrains and Energy Systems Technology Conference (2022-10-31 : Himeji, Japan)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2022

Summary:

Industries are currently going through "The Fourth Industrial Revolution," as professionals have called it "Industry 4.0" (I4.0). Integration of physical and digital systems for the product life cycle mainly concerns Industry 4.0. With the appearance of I4.0, the concept of prediction management has become an unavoidable tendency in the framework of big data and smart manufacturing. At the same time, it offers a reliable solution for handling test fatigue failures. AI and its key technologies play an essential role - Based on these collected data types, machine learning algorithms can be applied for automated failure detection and diagnosis. However, it is a bit difficult to select appropriate machine learning (ML) techniques, type of data, data size, and equipment to apply ML in industrial systems. Selection of inappropriate technique, dataset, and data size may cause time loss and infeasible result prediction. Therefore, this study aims to present a comprehensive case study of predicting the testing failure using ML techniques.This work presents a novel approach for different parameter- based fatigue failure (rig testing failure) characterization using artificial intelligence (AI). The deep learning algorithm is trained on carefully collected physical testing data (historical data), which helps in predicting the new product development testing failure cycles based on basic design parameters available at the start of the program such as loading, component dimensions, distances, and inclination angle, et cetera Rig testing reveals the testing cycles which indicate either failure or non-failure of the component (depending upon the passing criteria). Thus, every driveline component subjected to this research work generates at least one data set (testing values from AI). Based on this study, a conservative failure prediction accuracy of 88% is achieved. So, this methodology is pioneering to predict fatigue failure without - With expert knowledge of evaluation procedures, the developed AI approach enables quick and reliable prediction of fatigue failure of components based on elementary key design parameters which can reduce the overall design cycle time

Notes:

Vendor supplied data

Publisher Number:

2022-32-0014

Access Restriction:

Restricted for use by site license