A Framework for Product Life Cycle Management Based Digital Twin Implementation in the Aerospace Industry

IF 1.5 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Applied Stochastic Models in Business and Industry Pub Date : 2025-02-19 DOI:10.1002/asmb.70001
Busra Oksuz Gurdal, Ozlem Muge Testik
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Abstract

As an emerging technology, digital twin (DT) studies are gaining momentum in both academia and industry. Specifically, the aerospace industry can benefit significantly from the implementation of DT technology since its products and processes are complex, technically challenging, and costly. DTs enable a comprehensive technology integration capacity and holistic approach in the product life cycle. However, for simplification, implementations of DT to processes in the aerospace industry are often handled independently without integration with other related processes. In this study, we propose a methodological framework to integrate different processes throughout the essential parts of aircraft's life cycle. In pursuit of creating a DT of the system for managing the life cycle of aircraft, all aspects and processes have been thoroughly examined. Ten main components for the management of DTs are identified. Statistical and stochastic approaches for enhancing the analytical capabilities of DTs are discussed. Within the scope of Product Life Cycle Management and from the perspective of Systems Engineering, we advocate creating the DT of an aircraft by combining the DTs for each component through a digital thread.

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航空航天工业中基于数字孪生的产品生命周期管理框架
作为一项新兴技术,数字孪生(DT)研究在学术界和工业界都获得了蓬勃发展。具体来说,航空航天工业可以从DT技术的实施中获益良多,因为其产品和工艺复杂,技术上具有挑战性,而且成本高昂。dt能够在产品生命周期中实现全面的技术集成能力和整体方法。然而,为了简化,航空航天工业中流程的DT实现通常是独立处理的,而不与其他相关流程集成。在本研究中,我们提出了一个方法框架来整合飞机生命周期的各个重要部分的不同过程。为了创建一个管理飞机生命周期的DT系统,所有方面和过程都经过了彻底的检查。确定了dt管理的十个主要组成部分。讨论了提高dt分析能力的统计和随机方法。在产品生命周期管理的范围内,从系统工程的角度来看,我们提倡通过数字线程将每个组件的DT组合在一起来创建飞机的DT。
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来源期刊
CiteScore
2.70
自引率
0.00%
发文量
67
审稿时长
>12 weeks
期刊介绍: ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.
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