Process parameter and logic extraction for complex manufacturing job shops leveraging network analytics and Digital Twin modelling techniques

被引：3

作者：

Gyulai, David ^{[1
]}

Ikeuchi, Kiyoko ^{[1
]}

Bergmann, Julia ^{[2
,3
]}

Rao, Suraj ^{[1
]}

Kadar, Botond ^{[4
]}

机构：

[1] Western Digital Corp, Adv Analyt Off, San Jose, CA 95119 USA

[2] Eotv Lorand Res Network ELKH, Inst Comp Sci & Control SZTAK, Budapest, Hungary

[3] Eotvos Lorand Univ, Doctoral Sch Informat, Budapest, Hungary

[4] EP InnoLabs Nofit Ltd, Budapest, Hungary

来源：

CIRP ANNALS-MANUFACTURING TECHNOLOGY | 2023年 / 72卷 / 01期

关键词：

Digital twin; Simulation; Modelling;

D O I：

10.1016/j.cirp.2023.03.032

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In operations management, the benefit of simulating manufacturing processes with data-driven models has been proven in scenario-based capacity and performance analytics. The availability of data is typically not a barrier anymore, as process parameters can be accessed and modelled relatively easily, however, the system logic representation and extraction has remained challenging. In this paper, a systematic method is presented to build prediction models for a complex manufacturing system that extracts not only the process parameters, but also the routing and operating logic. The approach combines network analytics and statistical modelling techniques to automate the model building and scenario analytics.& COPY; 2023 CIRP. Published by Elsevier Ltd. All rights reserved.

引用

页码：417 / 420

页数：4

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