Implementation of Digital Twin for Engine Block Manufacturing Processes

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.

[1]  Kwangyeol Ryu,et al.  Digital Twin concept for smart injection molding , 2018 .

[2]  Karel Kruger,et al.  A six-layer architecture for the digital twin: a manufacturing case study implementation , 2019, Journal of Intelligent Manufacturing.

[3]  Chao Liu,et al.  Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems , 2020, Journal of Manufacturing Systems.

[4]  Yu Zheng,et al.  An application framework of digital twin and its case study , 2018, Journal of Ambient Intelligence and Humanized Computing.

[5]  Andrew Y. C. Nee,et al.  Enabling technologies and tools for digital twin , 2019 .

[6]  Erika Sujová,et al.  Application of Digitization Procedures of Production in Practice , 2019 .

[7]  Felix T.S. Chan,et al.  Defining a Digital Twin-based Cyber-Physical Production System for autonomous manufacturing in smart shop floors , 2019, Int. J. Prod. Res..

[8]  Jin Cao,et al.  A Referenced Cyber Physical System for Compressor Manufacturing , 2020 .

[9]  Elisa Negri,et al.  Review of digital twin applications in manufacturing , 2019, Comput. Ind..

[10]  Jason Yon,et al.  Characterising the Digital Twin: A systematic literature review , 2020, CIRP Journal of Manufacturing Science and Technology.

[11]  Qiang Liu,et al.  Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system , 2019, Int. J. Prod. Res..

[12]  Arne Bilberg,et al.  Digital twin driven human–robot collaborative assembly , 2019, CIRP Annals.

[13]  Bin He,et al.  Digital twin-based sustainable intelligent manufacturing: a review , 2020, Advances in Manufacturing.

[14]  Chao Zhang,et al.  Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing , 2019, Int. J. Prod. Res..

[15]  Prateep Misra,et al.  Digital twin: current scenario and a case study on a manufacturing process , 2020, The International Journal of Advanced Manufacturing Technology.

[16]  Zhong Fan,et al.  Digital Twin: Enabling Technologies, Challenges and Open Research , 2019, IEEE Access.

[17]  Jianhua Liu,et al.  Digital twin-based smart production management and control framework for the complex product assembly shop-floor , 2018, The International Journal of Advanced Manufacturing Technology.

[18]  Nasser Jazdi,et al.  A concept in synchronization of virtual production system with real factory based on anchor-point method , 2018 .

[19]  Jie Zhang,et al.  The modelling and operations for the digital twin in the context of manufacturing , 2018, Enterp. Inf. Syst..

[20]  Soemon Takakuwa,et al.  Application of IoT-Aided Simulation to Manufacturing Systems in Cyber-Physical System , 2019, Machines.

[21]  Xin Chen,et al.  A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line , 2017, IEEE Access.