Combining data-driven and model-driven methods to support digital-twin engineering design: A case study for the (Re)design optimization of a quadcopter

Abstract

Digital twins have emerged as one of the leading technologies in Industry 4.0. Their applications in manufacturing, agriculture, energy, and healthcare are gaining significant attention due to their diverse capabilities. Despite this growing interest, there is limited literature on applications of digital twins in engineering design and product optimization. To address this gap, this study aims to develop a digital twin framework for optimizing a specific component, using a case study of an Unmanned Aerial Vehicle (UAV) arm. The primary objective of this paper is to conceptualize and establish methodological steps to build a digital twin using diverse sources of data. In the process of developing the digital twin, different data-driven methods, such as neural network-based models, Dynamic Mode Decomposition (DMD), and Response Surface model (RSM), are evaluated based on different metrics. By using the developed digital twin, the study seeks to enhance the design for the UAV arm under varying working conditions. Results show how this digital twin enables design optimization, real-time monitoring, and performance optimization. Additionally, this study highlights the technical challenges and fundamental elements required to form a digital twin that assists in the engineering design process. Surrogate models are employed to mirror the physical behavior of a UAV arm within the simulation environment. A digital twin, designed with simulation data, is created to perform design optimization. This digital twin enables simulation-driven design and monitoring, aiming to optimize performance under varying conditions while reducing trial-and-error costs in engineering design and manufacturing. The findings confirm the effectiveness of the digital twin in optimizing designs.