A framework for developing engineering design ontologies within the aerospace industry

Abstract

This paper presents a framework for developing engineering design ontologies within the aerospace industry. The aim of this approach is to strengthen the modularity and reuse of engineering design ontologies to support knowledge management initiatives within the aerospace industry. Successful development and effective utilisation of engineering ontologies strongly depends on the method/framework used to develop them. Ensuring modularity in ontology design is essential for engineering design activities due to the complexity of knowledge that is required to be brought together to support the product design decision-making process. The proposed approach adopts best practices from previous ontology development methods, but focuses on encouraging modular architectural ontology design. The framework is comprised of three phases namely: (1) Ontology design and development; (2) Ontology validation and (3) Implementation of ontology structure. A qualitative research methodology is employed which is composed of four phases. The first phase defines the capture of knowledge required for the framework development, followed by the ontology framework development, iterative refinement of engineering ontologies and ontology validation through case studies and experts' opinion. The ontology-based framework is applied in the combustor and casing aerospace engineering domain. The modular ontologies developed as a result of applying the framework and are used in a case study to restructure and improve the accessibility of information on a product design information-sharing platform. Additionally, domain experts within the aerospace industry validated the strengths, benefits and limitations of the framework. Due to the modular nature of the developed ontologies, they were also employed to support other project initiatives within the case study company such as rolebased computing (RBC), IT modernisation activity and knowledge management implementation across the sponsoring organisation. The major benefit of this approach is in the reduction of man-hours required for maintaining engineering design ontologies. Furthermore, this approach strengthens reuse of ontology knowledge and encourages modularity in the design and development of engineering ontologies.

Ontologies in the context of product lifecycle management: state of the art literature review

“…Ontology supports data integration through the application of Linked Data principles El Kadiri, Milicic, and Kiritsis (2013) Data/ information exchange Ontology encompasses the model and the data and thus can be seamlessly exchanged among different systems on a global basis based on standardised representation languages (OWL/RDF/ RDFS) Rico et al (2014), Dartigues (2003), Dartigues et al (2007), Abdul-Ghafour (2009), Lin and Harding (2007), and Yoo and Kim (2002) Information modelling Ontology enables information modelling of the product throughout its entire lifecycle and supports expressing a shared understanding of a domain as a common source of knowledge Barbau et al (2012), Vegetti, Henning, andLeone (2005), Giménez et al (2008), Panetto, Dassisti, andTursi (2012), Kiritsis (2011), Matsokis and Kiritsis (2010), Milicic et al (2012) and Perdikakis et al (2012) Knowledge engineering Ontology supports capturing, storing and retrieving knowledge taking advantages from reasoning mechanisms to infer hidden and tacit facts Sanya and Shehab (2014), Giménez et al (2008), Kiritsis (2011), Matsokis andKiritsis (2010), Lutzenberger, Klein, andThoben (2013), Panetto, Dassisti, and Tursi (2012), Chen, Chen, andChu (2009), Jiang, Peng, and, Lin and Harding (2007), Fortineau (2013), Milicic et al (2012aMilicic et al ( , 2013, and Grosse, Milton-Benoit, and Wileden (2005) International Journal of Production Research 7 models. This conversion results in semantic loss in information representation, which requires a preliminary knowledge of the conceptual model in order to operate on the database.…”

“…Grosse, Milton-Benoit, and Wileden (2005) propose to implement a formal set of ontologies for classifying analysis modelling knowledge to facilitate knowledge exchange, reuse and interoperability of analysis models in engineering tasks. Similarly, Sanya and Shehab (2014) provide an approach to strengthen the modularity and reuse of engineering design ontologies to support knowledge management in the aerospace industry. Lutzenberger, Klein, and Thoben (2013) propose a concept of central knowledge-based system in order to deal with knowledge-based engineering (KBE) approaches in the context of smart products.…”

Pengembangan Standar Nasional Indonesia (Sni) Pangan Fungsional Untuk Mengurangi Resiko Obesitas

“…Melalui pendekatan FACTS pada saat menyusun standar, dapat diperoleh informasi tambahan yang formal dan terstruktur, sehingga standar yang akan dikembangkan dapat diterima oleh semua pemangku kepentingan (Aristyawati et al, 2016). Metode FACTS memungkinkan memfasilitasi pengembangan standar dengan melalui sejumlah fase yaitu konsepsi, pengembangan, penerapan dan pengujian (Sanya & Shehab, 2015).…”

Verification and validation: a novel product lifecycle management-based approach

“…2) Data Exchange: According to multiple studies a V&V plan must unambiguously describe and define the data flow and take into consideration this exchange (Weissman, Petrov and Gupta, 2011;Shehab et al, 2013;Sanya and Shehab, 2015). Such a plan can indicate overlapping activities, prevent non-needed processes and keep production under the welldefined quality acceptance limits.…”