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A Hybrid Approach for Predictive Modeling of KPIs in CNC Machining Operations

Vishnu, VS and George Varghese, K and Gurumoorthy, B (2023) A Hybrid Approach for Predictive Modeling of KPIs in CNC Machining Operations. In: 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2022, 13-15 July 2022, Naples, pp. 566-571.

CIRP-ICME2022_118_566-571_2023.pdf - Published Version

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Official URL: https://doi.org/10.1016/j.procir.2023.06.097


In a CNC machining operation, key performance indicators (KPIs) of process, such as machining time, quality, and energy consumption, vary with cutting parameters. This paper explains a methodology for building physics-guided data-driven models for predicting these process KPIs in CNC machining operations from the planning, machining, and quality data. These physics-guided data-driven models are developed by combining data-driven and physics-based models of machining operations. Using hybrid physics-ML method, predictive modelling of energy consumption and surface roughness in CNC milling operation is also explained by conducting experiments. Finally, accuracies obtained by these models are compared with respective physics-based and data-driven models. © 2023 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 authors.
Keywords: Benchmarking; Energy utilization; Machining centers; Surface roughness, CNC machining; Data analytics; Data-driven model; Hybrid approach; Key performance indicators; Machining operations; Machining time; Physic-guided data-driven modeling; Predictive models; Time consumption, Data Analytics
Department/Centre: Division of Mechanical Sciences > Centre for Product Design & Manufacturing
Date Deposited: 18 Dec 2023 04:49
Last Modified: 18 Dec 2023 04:49
URI: https://eprints.iisc.ac.in/id/eprint/83504

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