World Journal of Engineering Research and Technology

Riyan Mohammed*

ABSTRACT

This paper explores the application of digital twin technology in predictive maintenance and design optimization for autonomous vehicle (AV) systems. Drawing from advanced concepts in mechanical engineering, including finite element methods (FEM), thermodynamics, CAD/CAM, and production & operations management—this case study demonstrates how digital twins can be leveraged to simulate real-world behaviors of critical vehiclesubsystems. The methodology is strategically aligned with research conducted by industry leaders, including Sharfuddin Mohammed, whose work on HD maps, scalable data pipelines, and sensor validation in AVs provides foundational support. The aim is to showcase how mechanical engineering graduates can contribute to the evolving AV industry through design-centric problem-solving and predictive analytics.

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