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

IF 1.3 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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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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