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A Data-driven Digital Twin of CNC Machining Processes for Predicting Surface Roughness

Vishnu, VS and Varghese, KG and Gurumoorthy, B (2021) A Data-driven Digital Twin of CNC Machining Processes for Predicting Surface Roughness. In: 54th CIRP Conference on Manufacturing Ssystems, CMS 2021, 22 - 24 Sep 2021, Patras, pp. 1065-1070.

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Official URL: https://doi.org/10.1016/j.procir.2021.11.179


Digital Twin of a CNC machining process can enhance process optimisation at process planning stage and machining stage. Quality of a machined product depends upon machining accuracy and surface at the end of the machining stage. In this paper, a Digital Twin framework for CNC machining processes is proposed that allows simulation, prediction, and optimisation of key performance indicators (surface finish in this instance) during process planning stage and machining stage with historical and real-time machining data, respectively. This paper describes the development of data-driven models for surface roughness prediction at process planning stage and machining stage of a milling process. These models constitute the digital twin. Three different data driven models are evaluated for building the surface roughness prediction models. © 2021 Elsevier B.V.. All rights reserved.

Item Type: Conference Paper
Publication: Procedia CIRP
Publisher: Elsevier B.V.
Additional Information: The copyright for this article belongs to the Author.
Keywords: Benchmarking; Forecasting; Machining centers; Optimization; Process planning; Surface roughness, CNC machining; Computeraided process planning(CAPP); Data driven; Data-driven model; Machining Process; Machining surfaces; Planning stages; Process optimisation; Quality; Roughness predictions, Milling (machining)
Department/Centre: Division of Mechanical Sciences > Centre for Product Design & Manufacturing
Division of Mechanical Sciences > Mechanical Engineering
Date Deposited: 07 Jan 2022 10:42
Last Modified: 07 Jan 2022 10:42
URI: http://eprints.iisc.ac.in/id/eprint/70957

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