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

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...

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Bibliographic Details
Published inProcedia CIRP Vol. 104; pp. 1065 - 1070
Main Authors Vishnu, V.S., Varghese, Kiran George, Gurumoorthy, B.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2021
Subjects
CNC machining
Computer-aided process planning
Data-driven modelling
Digital Twin
Quality
CNC machining
Data-driven modelling
Computer-aided process planning
Digital Twin
Quality
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Summary: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.
ISSN:2212-8271
2212-8271
DOI:10.1016/j.procir.2021.11.179