Guerra-Zubiaga, D.
, Colani, M.
, Richards, G.
, Cavens, K.
, Block, L.
, Olliff, G.
and Aksu, M.
(2025),
A Novel Digital Twin Model to Support Intelligent Robotic Manufacturing System According to Industry 4.0 Trends, Proceedings of the ASME 2024 International Mechanical Engineering Congress and Exposition (IMECE2024), Portland, OR, US, [online], https://doi.org/10.1115/IMECE2024-145821, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=958314
(Accessed February 28, 2025)
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A Novel Digital Twin Model to Support Intelligent Robotic Manufacturing System According to Industry 4.0 Trends
Published
Author(s)
David Guerra-Zubiaga, Melody Colani, Gershom Richards, Katrina Cavens, Logan Block, George Olliff,
Abstract
Data, Information, and knowledge models are vital research topics explored in recent years to support advanced manufacturing decisions through important systems. Today, there are several tools that assist in modeling these parameters, one of which is known as Knowledge-Based Engineering Systems (KBES). A KBES integrates different knowledge types to support decisions and allows the incorporation of multiple types of manufacturing and organization standards to work in a variety of business contexts. With the increased complexity of industrial manufacturing systems caused by Industrial 4.0 trends such as digital manufacturing and cyber-physical technology like digital twins (DTs), the use of KBES is becoming an essential tool in Project Life Management (PLM) to ensure DT design is suitable to preserve the integrity of industrial standards. This paper aims to utilize Industry 4.0 trends, ISO standards, and the CIMOSA framework to build a KBES that will assist in the development of a novel DT model. This model will support the design and implementation of a DT within the environment of an intelligent robotic manufacturing system. The proposed DT model will be useful in preserving industrial standards in DT development and aid as a reference for the foundation of DTs design in other manufacturing environments. A case study is performed applying the proposed DT model to the design and implementation of a DT within a basic pick-and-place system, proving its applicability and function in the support of intelligent robotic manufacturing systems.Proceedings Title
Proceedings of the ASME 2024 International Mechanical Engineering Congress and Exposition (IMECE2024)
Conference Dates
November 17-21, 2024
Conference Location
Portland, OR, US
Pub Type
Conferences
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Keywords
DIGITAL MANUFACTURING TWIN MODELS, DIGITAL MANUFACTURING TOOLS, INTELLIGENT MANUFACTURING, INDUSTRIAL INTERNET OF THINGS, MACHINE LEARNING
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Created January 25, 2025, Updated February 27, 2025