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G-code evaluation in CNC milling to predict energy consumption through Machine Learning

  • Computerized Numeric Control (CNC) plays an essential role in highly autonomous manufacturing systems for interlinked process chains for machine tools. NC-programs are mostly written in standardized G-code. Evaluating CNC-controlled manufacturing processes before their real application is advantageous due to resource efficiency. One dimension is the estimation of the energy demand of a part manufactured by an NC-program, e.g. to discover optimization potentials. In this context, this paper presents a Machine Learning (ML) approach to assess G-code for CNC-milling processes from the perspective of the energy demand of basic G-commands. We propose Latin Hypercube Sampling as an efficient method of Design of Experiments to train the ML model with minimum experimental effort to avoid costly setup and implementation time of the model training and deployment.

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Metadaten
Author:Anna-Maria Schmitt, Eddi Miller, Bastian Engelmann, Rafael Batres, Jan Schmitt
Persistent identifier:https://doi.org/10.1016/j.aime.2024.100140
Parent Title (English):Advances in Industrial and Manufacturing Engineering
Document Type:Article
Language:English
Year of publication:2024
Publishing Institution:Technische Hochschule Würzburg-Schweinfurt
Release Date:2024/05/14
Tag:CNC machine tools; Energy consumption; G-code; Machine Learning
Volume:2024
Issue:8
Institutes and faculty:Fakultäten / Fakultät Wirtschaftsingenieurwesen
Institute / Institut Digital Engineering (IDEE)
Licence (German):License LogoCreative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International
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