In Industry 4.0 era, rapid changes to the global landscape of manufacturing are transforming industrial plants in increasingly more complex digital systems. One of the most impactful innovations generated in this context is the "Digital Twin", a digital copy of a physical asset, which is used to perform simulations, health predictions and life cycle management through the use of a synchronized data flow in the manufacturing plant. In this paper, an innovative approach is proposed in order to contribute to the current collection of applications of Digital Twin in manufacturing: a Digital Shadow cloud-based application to enhance quality control in the manufacturing process. In particular, the proposal comprises a Digital Shadow updated on high performance computing cloud infrastructure in order to recompute the performance prediction adopting a variation of the computer-aided engineering model shaped like the actual manufactured part. Thus, this methodology could make possible the qualification of even not compliant parts, and so shift the focus from the compliance to tolerance requirements to the compliance to usage requirements. The process is demonstrated adopting two examples: the structural assessment of the geometry of a shaft and the one of a simplified turbine blade. Moreover, the paper presents a discussion about the implications of the use of such a technology in the manufacturing context in terms of real-time implementation in a manufacturing line and lifecycle management. Copyright (C) 2020 The Authors.

Santolamazza, A., Groth, C., Introna, V., Porziani, S., Scarpitta, F., Urso, G., et al. (2020). A digital shadow cloud-based application to enhance quality control in manufacturing. ??????? it.cilea.surplus.oa.citation.tipologie.CitationProceedings.prensentedAt ??????? 21st IFAC World Congress 2020 [10.1016/j.ifacol.2020.12.2809].

A digital shadow cloud-based application to enhance quality control in manufacturing

Groth C.;Introna V.;Valentini P. P.;Biancolini M. E.
2020-01-01

Abstract

In Industry 4.0 era, rapid changes to the global landscape of manufacturing are transforming industrial plants in increasingly more complex digital systems. One of the most impactful innovations generated in this context is the "Digital Twin", a digital copy of a physical asset, which is used to perform simulations, health predictions and life cycle management through the use of a synchronized data flow in the manufacturing plant. In this paper, an innovative approach is proposed in order to contribute to the current collection of applications of Digital Twin in manufacturing: a Digital Shadow cloud-based application to enhance quality control in the manufacturing process. In particular, the proposal comprises a Digital Shadow updated on high performance computing cloud infrastructure in order to recompute the performance prediction adopting a variation of the computer-aided engineering model shaped like the actual manufactured part. Thus, this methodology could make possible the qualification of even not compliant parts, and so shift the focus from the compliance to tolerance requirements to the compliance to usage requirements. The process is demonstrated adopting two examples: the structural assessment of the geometry of a shaft and the one of a simplified turbine blade. Moreover, the paper presents a discussion about the implications of the use of such a technology in the manufacturing context in terms of real-time implementation in a manufacturing line and lifecycle management. Copyright (C) 2020 The Authors.
21st IFAC World Congress 2020
Rilevanza internazionale
2020
Settore ING-IND/17 - IMPIANTI INDUSTRIALI MECCANICI
English
Digital Twin
quality control
Intelligent manufacturing systems
"As built" design
Life-cycle control
radial basis functions
Computer-aided engineering
mesh morphing
https://www.sciencedirect.com/science/article/pii/S2405896320335746?via=ihub
Intervento a convegno
Santolamazza, A., Groth, C., Introna, V., Porziani, S., Scarpitta, F., Urso, G., et al. (2020). A digital shadow cloud-based application to enhance quality control in manufacturing. ??????? it.cilea.surplus.oa.citation.tipologie.CitationProceedings.prensentedAt ??????? 21st IFAC World Congress 2020 [10.1016/j.ifacol.2020.12.2809].
Santolamazza, A; Groth, C; Introna, V; Porziani, S; Scarpitta, F; Urso, G; Valentini, Pp; Costa, E; Ferrante, E; Sorrentino, S; Capacchione, B; Rochette, M; Bergweiler, S; Poser, V; Biancolini, Me
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/278105
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