使用梯度提升机在有限元加工仿真中进行切削力预测的数据驱动方法

IF 3.3 Q2 ENGINEERING, MANUFACTURING
Tim Reeber, Jan Wolf, Hans-Christian Möhring
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引用次数: 0

摘要

由于计算性能不断提高,从而获得了更高的精度,通过有限元法(FEM)进行的切削模拟最近变得越来越重要。然而,这些模拟仍需耗费大量时间,因此无法在工业环境中对加工过程进行现场评估。这是由于加工过程的有限元模拟具有高度非线性的特点,需要大量的计算资源。另一方面,众所周知,机器学习方法可以捕捉复杂的非线性行为。切削模拟中应用最广泛的材料模型之一是约翰逊-库克材料模型,该模型对切削模拟的输出有很大影响,并促成了模型的非线性行为,但其对切削力的影响有时难以事先评估。因此,本研究旨在通过使用来自 Abaqus 的多个短时切削模拟数据集来捕捉材料模型的高度非线性行为,从而了解约翰逊-库克材料模型参数与恒定切削条件下产生的切削力之间的关系。这样做的目的是将复杂的切削条件与材料参数的关系封装在一个模型中,从而缩短模拟切削力的时间。共训练了五个不同的模型,并对其性能进行了评估。结果表明,梯度提升机器能最好地捕捉不同材料模型参数的影响,并能很好地预测切削力,还能深入了解材料参数与正交切削中的切削力和推力的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Data-Driven Approach for Cutting Force Prediction in FEM Machining Simulations Using Gradient Boosted Machines
Cutting simulations via the Finite Element Method (FEM) have recently gained more significance due to ever increasing computational performance and thus better resulting accuracy. However, these simulations are still time consuming and therefore cannot be deployed for an in situ evaluation of the machining processes in an industrial environment. This is due to the high non-linear nature of FEM simulations of machining processes, which require considerable computational resources. On the other hand, machine learning methods are known to capture complex non-linear behaviors. One of the most widely applied material models in cutting simulations is the Johnson–Cook material model, which has a great influence on the output of the cutting simulations and contributes to the non-linear behavior of the models, but its influence on cutting forces is sometimes difficult to assess beforehand. Therefore, this research aims to capture the highly non-linear behavior of the material model by using a dataset of multiple short-duration cutting simulations from Abaqus to learn the relationship of the Johnson–Cook material model parameters and the resulting cutting forces for a constant set of cutting conditions. The goal is to shorten the time to simulate cutting forces by encapsulating complex cutting conditions in dependence of material parameters in a single model. A total of five different models are trained and the performance is evaluated. The results show that Gradient Boosted Machines capture the influence of varying material model parameters the best and enable good predictions of cutting forces as well as deliver insights into the relevance of the material parameters for the cutting and thrust forces in orthogonal cutting.
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来源期刊
Journal of Manufacturing and Materials Processing
Journal of Manufacturing and Materials Processing Engineering-Industrial and Manufacturing Engineering
CiteScore
5.10
自引率
6.20%
发文量
129
审稿时长
11 weeks
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