ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

A data-driven digital twin framework for key performance indicators in CNC machining processes

Vishnu, VS and Varghese, KG and Gurumoorthy, B (2023) A data-driven digital twin framework for key performance indicators in CNC machining processes. In: International Journal of Computer Integrated Manufacturing .

Full text not available from this repository.
Official URL: https://doi.org/10.1080/0951192X.2023.2177741


This paper presents a data-driven digital twin (DT) framework that predicts key performance indicators (KPIs) in a CNC machining environment. The decision-makers can use these predicted KPIs in the CNC machining process flow to better choose cutting parameters to accomplish the required KPIs. Those beneficiaries would be the process planner in the process planning stage and the machine operator in the machining stage. The cutting parameters affect major performance KPIs such as machining time, quality, and energy consumption. So, correctly selected cutting parameters can improve KPIs in CNC machining operations. In this paper, the two KPIs considered for building predictive models, and their application in the proposed DT with experimental data are energy and surface roughness. The data for building the predictive models for a CNC milling process are obtained through experiments. This work also illustrates the choice of predictive modelling methods in both the stages of CNC machining and its outcomes. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Item Type: Journal Article
Publication: International Journal of Computer Integrated Manufacturing
Publisher: Taylor and Francis Ltd.
Additional Information: The copyright for this article belongs to Taylor and Francis Ltd.
Keywords: Benchmarking; Decision making; Machining centers; Milling (machining); Surface roughness; Turning, CNC machining; Cutting parameters; Data driven; Data-driven model; Decision makers; Key performance indicators; Machining Process; Planning stages; Predictive models; Process flows, Energy utilization
Department/Centre: Division of Mechanical Sciences > Centre for Product Design & Manufacturing
Date Deposited: 17 Mar 2023 09:52
Last Modified: 17 Mar 2023 09:53
URI: https://eprints.iisc.ac.in/id/eprint/81025

Actions (login required)

View Item View Item