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

IF 3.9 Q2 ENGINEERING, INDUSTRIAL Advances in Industrial and Manufacturing Engineering Pub Date : 2024-04-07 DOI:10.1016/j.aime.2024.100140
Anna-Maria Schmitt , Eddi Miller , Bastian Engelmann , Rafael Batres , Jan Schmitt
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引用次数: 0

Abstract

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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通过机器学习评估数控铣削中的 G 代码以预测能耗
计算机数控(CNC)在高度自主的制造系统中发挥着至关重要的作用,可用于机床相互关联的工艺链。数控程序大多采用标准化的 G 代码编写。在实际应用之前对数控制造工艺进行评估,有利于提高资源利用效率。其中一个方面是估算由数控程序制造的零件的能源需求,例如发现优化潜力。在此背景下,本文提出了一种机器学习(ML)方法,从基本 G 命令的能源需求角度评估数控铣削过程的 G 代码。我们提出了拉丁超立方采样(Latin Hypercube Sampling)这一高效的实验设计方法,以最小的实验工作量训练 ML 模型,从而避免模型训练和部署过程中昂贵的设置和实施时间。
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来源期刊
Advances in Industrial and Manufacturing Engineering
Advances in Industrial and Manufacturing Engineering Engineering-Engineering (miscellaneous)
CiteScore
6.60
自引率
0.00%
发文量
31
审稿时长
18 days
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