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

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.

Automated summary

This paper presents a data-driven digital twin framework that predicts KPIs in CNC machining. The predicted KPIs can assist decision-makers in choosing cutting parameters to achieve the required KPIs. The paper focuses on two KPIs, energy and surface roughness, and their application in the proposed digital twin using experimental data. The work also discusses the choice of predictive modelling methods and their outcomes in CNC machining.