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.
Author: | Anna-Maria Schmitt, Eddi Miller, Bastian Engelmann, Rafael Batres, Jan Schmitt |
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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): | Creative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International |