Session: 03-11-01: Future of Smart Manufacturing

Paper Number: 145821

145821 - A Novel Digital Twin Model to Support Intelligent Robotic Manufacturing System According to Industry 4.0 Trends. 

Data, information, and knowledge models are vital research topics explored in the last few years to support advanced manufacturing decisions. Today, there are various tools, methods, and standards that make the modeling of these three parameters possible to create these important systems. For example, it is required to explore data, information, and knowledge models in the design and development of a Knowledge-Based Engineering System (KBES) to support advanced manufacturing decision-making. In this case, one must identify the differences between data, information, and knowledge as well as their respective modeling aspects. Data are simply symbols with no context and no relationships, information is data that has meaning in a given context, and knowledge is information that together can support decisions. In addition, there are explicit, tacit, and implicit knowledge types, among others. Explicit knowledge is the knowledge captured, for example in tables, procedures, and graphs (among other formats). Tacit knowledge is a very complex knowledge type difficult to capture and share. Implicit knowledge is a bridge between explicit and tacit knowledge, later in the paper will be explained in more detail. Today, KBES can be developed according to standards (such as ISO) and use data, information, and knowledge models to support manufacturing decisions. The standards are required in KBES development because different organizations can create these systems and interact with them. Some KBES use frameworks such as PERA (Purdue Enterprise Reference Architecture), CIMOSA (Computer Integrated Manufacturing Open System Architecture), and GERAM (Generalized Enterprise Reference Architecture and Methodology) in addition to standards to be developed and implemented properly In addition to the standards and the frameworks some researchers use approaches for the development of KBES and better implementation in industry. Using the standardization of KBES as a reference. The motivation behind this research is to establish standardized protocols and definitions for showcasing novel digital twin models and frameworks that support intelligent robotics manufacturing systems in line with Industry 4.0 trends. The novelty contribution of this paper lies in demonstrating not only the new digital twin model based on existing standards but also in identifying and defining how Industry 4.0 trends like Industrial Internet of Things, Machine Learning, and Cloud Manufacturing, among others, can enhance the usefulness of a digital twin model in aiding decision-making for the development of intelligent robotic manufacturing systems. Digital Twins is a hot trending topic throughout technical fields, including manufacturing, but there is a need to standardize the differences between Digital Twin models, frameworks, and approaches to create consistent, valuable digital twins to be able to support intelligent manufacturing integration according to Industry 4.0 trends. For example, how can two differing manufacturing digital twins’ frameworks communicate with each other? As KBES standardized how manufacturers organize engineering system data, the manufacturing industry needs to standardize the definition of models, frameworks and approaches for uniformity across the industry. This research presents some definitions of Industry 4.0 trends not only from a literature review but also according to some standards. In addition, this research applies these definitions in a basic digital twin case study using a pick-and-place robotic operation. The digital twin case study uses some digital manufacturing tools and robotic automation aspects. This research demonstrates how the novel digital twin model makes it possible to support intelligent robotics manufacturing systems decisions according to industry 4.0 trends.

Presenting Author: David Guerra-Zubiaga Kennesaw State University

Presenting Author Biography: David A. Guerra-Zubiaga
Siemens Endowed Professor of Mechatronics
Kennesaw State University
Dr. Guerra-Zubiaga has 11 years of industry experience and 13 years of academic experience. He has led important international industrial projects with 14.2 million USD, as total research income gained. In 2014 and 2016, he obtained a $340 Million In-Kind Software Grant from Siemens PLM Software. He published 2 patents, 1 book, and more than 100 international papers; and he directed 25 postgraduate theses. Dr. Guerra-Zubiaga has been a senior associate editor (North America) for the International Journal of Computer Integrated Manufacturing since 2011. He has been a topic organizer for ASME-IMECE since 2017 and today is a Track organizer at the advanced manufacturing track. Today Dr. Guerra-Zubiaga is an Associate Professor in the Robotics and Mechatronics Engineering Department at Kennesaw State University, and he is a Siemens Endowed Professor of Mechatronics.

Authors:

David Guerra-Zubiaga Kennesaw State University
Melody Colani Kennesaw State University
Gershom Richards Kennesaw State University
Katrina Cavens Kennesaw State University
Logan Block Kennesaw State University
George Ollif Kennesaw State University
Murat Aksu National Institute of Standards and Technologies