Machine Learning Driven Prediction of Residual Stresses for the Shot Peening Process Using a Finite Element Based Grey-Box Model Approach
Machine Learning Driven Prediction of Residual Stresses for the Shot Peening Process Using a Finite Element Based Grey-Box Model Approach
复制标题
使用基于有限元的灰盒模型方法对喷丸过程的残余应力进行机器学习驱动的预测
DOI：
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发表时间：
2021
影响因子：
3.2
通讯作者：
M. Stockinger
中科院分区：
文献类型：
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作者：
B. J. Ralph;Karin Hartl;Marcel Sorger;Andreas Schwarz;M. Stockinger
喷丸工艺是增强金属加工环境中承载部件疲劳强度的常用程序。最佳工艺参数的确定通常是通过昂贵的实际实验来进行的。使用不同参数预测残余应力分布的有效方法是有限元分析。然而，不可能在合理的模拟中包含材料物理行为和工艺条件的所有影响因素。因此，数据驱动模型与实验数据相结合往往会对所得过程模型的准确性产生显着的优势。因此，本文描述了使用二维几何有限元建模方法的灰箱模型的开发。基于该模型，开发了一个Python框架，能够预测常见喷丸场景的残余应力。这种基于白盒的模型充当本工作中引入的机器学习技术的初始状态。由此产生的算法能够通过调整初始模型来添加来自实际残余应力实验的输入数据，从而稳定提高精度。为了演示实际用途，开发了相应的图形用户界面，能够根据用户要求的残余应力推荐喷丸参数。
The shot peening process is a common procedure to enhance fatigue strength on load-bearing components in the metal processing environment. The determination of optimal process parameters is often carried out by costly practical experiments. An efficient method to predict the resulting residual stress profile using different parameters is finite element analysis. However, it is not possible to include all influencing factors of the materials’ physical behavior and the process conditions in a reasonable simulation. Therefore, data-driven models in combination with experimental data tend to generate a significant advantage for the accuracy of the resulting process model. For this reason, this paper describes the development of a grey-box model, using a two-dimensional geometry finite element modeling approach. Based on this model, a Python framework was developed, which is capable of predicting residual stresses for common shot peening scenarios. This white-box-based model serves as an initial state for the machine learning technique introduced in this work. The resulting algorithm is able to add input data from practical residual stress experiments by adapting the initial model, resulting in a steady increase of accuracy. To demonstrate the practical usage, a corresponding Graphical User Interface capable of recommending shot peening parameters based on user-required residual stresses was developed.
DOI：
10.1016/j.ijmachtools.2011.07.005
发表时间：
2012-02-01
影响因子：
14
作者：
Mohamed, Mohamed S.;Foster, Alistair D.;Dean, Trevor A.
通讯作者：
Dean, Trevor A.