AI-Driven Digital Twin for Enhanced Suspension Assembly Testing in Automotive Vehicles

This paper presents the development and application of a digital twin for the suspension assembly of Automotive vehicle, a critical component in evaluating the performance and durability of automotive systems. The digital twin, a virtual replica of the physical suspension assembly, is created using advanced simulation techniques and physical test data integration. By leveraging machine learning algorithms, the digital twin provides a comprehensive and dynamic model that accurately reflects the behavior and performance of the physical system under various conditions. The primary objective of this study is to enhance the accuracy and efficiency of suspension assembly testing by enabling predictive maintenance, real-time monitoring, and optimization of test parameters. The digital twin allows for the identification of potential issues before they occur, reducing downtime and maintenance costs. Additionally, it facilitates the exploration of different test scenarios and conditions without the need for extensive physical testing, thereby saving time and resources. Our results demonstrate that the digital twin can effectively replicate the physical suspension assembly of Automotive vehicle, providing valuable insights into its performance and reliability. This research highlights the potential of digital twin technology in revolutionizing traditional testing methods, offering a scalable and cost-effective solution for improving the design, testing, and maintenance of automotive components.