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  1. A Product Life Cycle Ontology for Additive Manufacturing.Munira Mohd Ali, Rahul Rai, J. Neil Otte & Barry Smith - 2019 - Computers in Industry 105:191-203.
    The manufacturing industry is evolving rapidly, becoming more complex, more interconnected, and more geographically distributed. Competitive pressure and diversity of consumer demand are driving manufacturing companies to rely more and more on improved knowledge management practices. As a result, multiple software systems are being created to support the integration of data across the product life cycle. Unfortunately, these systems manifest a low degree of interoperability, and this creates problems, for instance when different enterprises or different branches of an enterprise interact. (...)
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  2. An Ontological Approach to Representing the Product Life Cycle.J. Neil Otte, Dimitris Kiritsi, Munira Mohd Ali, Ruoyu Yang, Binbin Zhang, Ron Rudnicki, Rahul Rai & Barry Smith - 2019 - Applied Ontology 14 (2):1-19.
    The ability to access and share data is key to optimizing and streamlining any industrial production process. Unfortunately, the manufacturing industry is stymied by a lack of interoperability among the systems by which data are produced and managed, and this is true both within and across organizations. In this paper, we describe our work to address this problem through the creation of a suite of modular ontologies representing the product life cycle and its successive phases, from design to end of (...)
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  3. Development of a Manufacturing Ontology for Functionally Graded Materials.Francesco Furini, Rahul Rai, Barry Smith, Georgio Colombo & Venkat Krovi - 2016 - In Proceedings of International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE).
    The development of manufacturing technologies for new materials involves the generation of a large and continually evolving volume of information. The analysis, integration and management of such large volumes of data, typically stored in multiple independently developed databases, creates significant challenges for practitioners. There is a critical need especially for open-sharing of data pertaining to engineering design which together with effective decision support tools can enable innovation. We believe that ontology applied to engineering (OE) represents a viable strategy for the (...)
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