A semantic-driven tradespace framework to accelerate aircraft manufacturing system design

Abstract

During the design phase of an aircraft manufacturing system, different industrial scenarios need to be evaluated according to key performance indicators to achieve the optimal system performance. It is a highly complex process involving multidisciplinary stakeholders, various digital tools and protocols. To address the digital discontinuity challenge during this process, this paper proposes a tradespace framework based on semantic technology and Model-Based Systems Engineering. It aims at functionality integration of requirement management, architecture definition, manufacturing system design, solution verification and visualization. An application ontology is developed to integrate assembly system domain knowledge, industrial requirements and system architecture model information. The proposed framework is implemented in a case study to support the fuselage orbital joint process design, which is part of the aircraft Final Assembly Line. A toolchain is presented to support the implementation, which consists of a set of enabling software corresponding to the functional modules of the framework. Different manufacturing system architectures are first designed by industrial system engineers supported by the application ontology semantic-driven tradespace framework stored in a graph database. They are then analyzed through Discrete Event Simulations and 3D simulations. The simulation results are presented through a web-based portal to show the key performance values of each architecture. This study serves as a part of the proofof-concept of the recently proposed Cognitive Digital Twin concept.

Model‐Based Framework for Data and Knowledge‐Driven Systems Architecting Demonstrated on a Hydrogen‐Powered Concept Aircraft

“…The framework of this paper considers existing model-based approaches using formalized knowledge as foundation. An ontology for managing formalized knowledge and performing design space exploration using an MBSE approach is described by Zheng et al (2021). Fuchs et al (2021) describe a method where knowledge, requirements, and system interrelation are used within models to automatically design aircraft cabin reducing development time.…”

“…Since numerous aircraft using these technologies are already in operation, detailed knowledge, that is, information, data, and experiences about existing OBS architectures and technologies, is available. It can be used as a foundation for development of novel concept aircraft and systems architectures (Zheng et al 2021). Knowledge is commonly available as literature or technical documentations in an unorganized, non-machine-readable form.…”

Model‐based Framework for Data and Knowledge‐Driven Systems Architecting Demonstrated on a Hydrogen‐Powered Concept Aircraft

“…Using KBE, an engineer has more time to focus on creative design tasks during conceptual design (van der Laan, 2008). In addition, formalized knowledge and KBE are often an integrated part of MBSE approaches using central or distributed data storage (Zheng et al, 2021). However, since knowledge is often unstructured, formalizing knowledge is a time consuming process reasonable only for systems with significant uncertainty, complexity, and long development times (Verhagen et al, 2012), which all apply to the process of aircraft conceptual design.…”

“…An ontology for managing formalized knowledge and performing design space exploration using an MBSE approach is described by ZHENG ET AL. (Zheng et al, 2021) (Sanya et al, 2014) provide an ontology-based, platform independent KBE framework to ensure conservation of formalized knowledge for many years by adapting existing model-based approaches. JUDT ET AL.…”

“…Since numerous aircraft using these technologies are already in operation, detailed knowledge, i.e., information, data, and experiences about existing OBS architectures and technologies, is available. It can be used as a foundation for development of novel concept aircraft and systems architectures (Zheng et al, 2021). Knowledge is commonly available as literature or technical documentations in an unorganized, non-machine-readable form.…”