Defining a Digital Twin-based Cyber-Physical Production System for autonomous manufacturing in smart shop floors

Ding, Kai; Chan, Felix T. S.; Zhang, Xudong; Zhou, Guanghui; Zhang, Fuqiang

doi:10.1080/00207543.2019.1566661

Cited by 328 publications

(130 citation statements)

References 37 publications

(43 reference statements)

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“…Liu et al [17] developed a DT-driven methodology for rapid individualized design of the automated flow-shop manufacturing system. Ding et al [18] presented the framework reference model of a DT-based Cyber-Physical Production System (DT-CPPS) and discussed in detail its configuring mechanism, operating mechanism, and real-time data-driven operations management. Zhou et al [19] proposed a general framework for a knowledge-driven DT manufacturing cell towards intelligent manufacturing, which could support autonomous manufacturing by an intelligent perceiving, simulating, understanding, predicting, optimizing, and controlling strategy.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Digital Twin for Engine Block Manufacturing Processes

et al. 2020

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The digital twin (DT) is undergoing an increase in interest from both an academic and industrial perspective. Although many authors proposed and described various frameworks for DT implementation in the manufacturing industry context, there is an absence of real-life implementation studies reported in the available literature. The main aim of this paper is to demonstrate feasibility of the DT implementation under real conditions of a production plant that is specializing in manufacturing of the aluminum components for the automotive industry. The implementation framework of the DT for engine block manufacturing processes consists of three layers: physical layer, virtual layer and information-processing layer. A simulation model was created using the Tecnomatix Plant Simulation (TPS) software. In order to obtain real-time status data of the production line, programmable logic control (PLC) sensors were used for raw data acquisition. To increase production line productivity, the algorithm for bottlenecks detection was developed and implemented into the DT. Despite the fact that the implementation process is still under development and only partial results are presented in this paper, the DT seems to be a prospective real-time optimization tool for the industrial partner.

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Section: Literature Reviewmentioning

confidence: 99%

Implementation of Digital Twin for Engine Block Manufacturing Processes

et al. 2020

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“…The algorithms of this class consist of a target/outcome variable (or dependent variable) which is to be predicted by a given set of predictors (independent variables). This approach enables to classify and determine a list of system's defaults [23] with health indicators for each part of it [24]. The unsupervised learning [25], instead, discovers an internal representation from input data only.…”

Section: State Of Artmentioning

confidence: 99%

Data-Driven Pattern-Based Constructs Definition for the Digital Transformation Modelling of Collaborative Networked Manufacturing Enterprises

Semeraro

Lezoche

Panetto

et al. 2019

IFIP Advances in Information and Communication Technology

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The digital transformation of collaborative networked manufacturing enterprises requires building and applying digital models representing the set of resources and processes knowledge. Modelling such digital copy of the physical system to perform real-time validation and optimization is quite complex and thus needs a big amount of data and some modelling patterns representing the operational semantics of the modelled elements. Generally, the modelling action has a specific application type. For this reason, the core challenge of the digital transformation modelling is to create a modular "digital model", namely a decomposable and re-composable model, towards different applications. The authors propose an approach based on the combination of data-driven and modeldriven approaches, to identify and formalize modelling patterns, that combine for developing a modular executable model of the studied system.

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“…Todays' manufacturing era focuses on monitoring the process on shop-floor by utilizing various sensorial systems that are based on data collection [1][2][3]. The automated systems directly collect an enormous amount of performance data from the shop-floor ( Figure 1), and are stored into a repository, in a raw or accumulated form [4,5].…”

Section: Introductionmentioning

confidence: 99%

A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT

Papacharalampopoulos,

Giannoulis,

Stavropoulos

et al. 2020

Applied Sciences

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A dashboard application is proposed and developed to act as a Digital Twin that would indicate the Measured Value to be held accountable for any future failures. The current study describes a method for the exploitation of historical data that are related to production performance and aggregated from IoT, to eliciting the future behavior of the production, while indicating the measured values that are responsible for negative production performance, without training. The dashboard is implemented in the Java programming language, while information is stored into a Database that is aggregated by an Online Analytical Processing (OLAP) server. This achieves easy Key Performance Indicators (KPIs) visualization through the dashboard. Finally, indicative cases of a simulated transfer line are presented and numerical examples are given for validation and demonstration purposes. The need for human intervention is pointed out.

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Defining a Digital Twin-based Cyber-Physical Production System for autonomous manufacturing in smart shop floors

Cited by 328 publications

References 37 publications

Implementation of Digital Twin for Engine Block Manufacturing Processes

Implementation of Digital Twin for Engine Block Manufacturing Processes

Data-Driven Pattern-Based Constructs Definition for the Digital Transformation Modelling of Collaborative Networked Manufacturing Enterprises

A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT

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