TY - JOUR A1 - Schmitt, Anna-Maria A1 - Miller, Eddi A1 - Engelmann, Bastian A1 - Batres, Rafael A1 - Schmitt, Jan T1 - G-code evaluation in CNC milling to predict energy consumption through Machine Learning T2 - Advances in Industrial and Manufacturing Engineering N2 - 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. KW - Machine Learning KW - CNC machine tools KW - G-code KW - Energy consumption Y1 - 2024 UR - https://opus4.kobv.de/opus4-fhws/frontdoor/index/index/docId/5609 UR - https://doi.org/10.1016/j.aime.2024.100140 VL - 2024 IS - 8 ER -