Hybrid FE-ML model for turning of 42CrMo4 steel

IF 4.6 2区 工程技术 Q2 ENGINEERING, MANUFACTURING CIRP Journal of Manufacturing Science and Technology Pub Date : 2024-10-24 DOI:10.1016/j.cirpj.2024.10.003
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Abstract

Metal cutting processes contribute significant share of the added value of industrial products. The need for machining has grown exponentially with increasing demands for quality and accuracy, and despite of more than a century of research in the field, there are no reliable and accurate models that describe all the physical phenomena needed to optimize the machining processes. The scientific community has begun to explore hybrid methods instead of expanding the capabilities of individual modelling schemes, which has been more efficient than efficacious direction. Following this trend, we propose a hybrid finite element — machine learning method (FEML) for modelling metal cutting. The advantages of the FEML method are reduced need for experimental data, reduced computational time and improved prediction accuracy. This paper describes the FEML model, which uses a Coupled Eulerian Lagrangian (CEL) formulation and deep neural networks (DNN) from the TensorFlow Python library. The machining experiments include forces, chip morphology and surface roughness. The experimental data was divided into training dataset and validation dataset to confirm the model predictions outside the experimental data range. The hybrid FEML model outperformed the DNN and FEM models independently, by reducing the computational time, improving the average prediction error from 23% to 13% and reduced the need for experimental data by half.

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用于 42CrMo4 钢车削的 FE-ML 混合模型
金属切削加工在工业产品的附加值中占有重要份额。随着对质量和精度的要求不断提高,对机械加工的需求也呈指数级增长,尽管在该领域的研究已超过一个世纪,但仍没有可靠而准确的模型来描述优化机械加工过程所需的所有物理现象。科学界已开始探索混合方法,而不是扩大单个建模方案的能力,这已成为比效率更高的方向。顺应这一趋势,我们提出了一种用于金属切削建模的有限元-机器学习混合方法(FEML)。FEML 方法的优势在于减少了对实验数据的需求、缩短了计算时间并提高了预测精度。本文介绍了 FEML 模型,该模型使用了耦合欧拉格拉格朗日(CEL)公式和 TensorFlow Python 库中的深度神经网络(DNN)。加工实验包括力、切屑形态和表面粗糙度。实验数据分为训练数据集和验证数据集,以确认实验数据范围之外的模型预测结果。混合 FEML 模型的性能优于 DNN 和 FEM 模型,它减少了计算时间,将平均预测误差从 23% 提高到 13%,并将实验数据的需求量减少了一半。
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来源期刊
CIRP Journal of Manufacturing Science and Technology
CIRP Journal of Manufacturing Science and Technology Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
6.20%
发文量
166
审稿时长
63 days
期刊介绍: The CIRP Journal of Manufacturing Science and Technology (CIRP-JMST) publishes fundamental papers on manufacturing processes, production equipment and automation, product design, manufacturing systems and production organisations up to the level of the production networks, including all the related technical, human and economic factors. Preference is given to contributions describing research results whose feasibility has been demonstrated either in a laboratory or in the industrial praxis. Case studies and review papers on specific issues in manufacturing science and technology are equally encouraged.
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