.png){kind=icon}
返回
Gaussian process regression-driven deep drawing blank design method
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ijmecsci.2023.108898.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This research introduces a machine learning (ML)-based methodology for the optimal blank design of components manufactured through the deep drawing process, considering the interplay among material, process, and geometric parameters. The proposed blank design mapping function (BDMF) leverages a Gaussian process regression (GPR) ML model in conjunction with a radial basis function (RBF) kernel. This combination allows for correlating predictions with their standard deviations, capturing the estimations' quality effectively. The GPR model was trained using the results from a three-dimensional adaptive mesh-based finite element analysis (FEA) model, characterized by a fixed node count of 23 input parameters, an explicit solution scheme, and an average computational time of 270 s. Laboratory-scale experiments on an R47.5 mm flanged cup constructed from AISI304 steel and AA5754 aluminum alloy served to validate the FEA models and the proposed BDMF. The comparison between experimental outcomes and FEA results revealed maximum deviations of 13.3 % in the drawing force and 0.35 % for the earing profile over a 90 degrees segment. The comparison between experimental data and BDMF predictions for the sheet metal blank indicated average deviations of 0.015 mm (or 1.3 %) in estimating thickness and 0.12 mm (or 0.25 %) in predicting the outer radius. Application of the BDMF to four additional flanged geometries with varying shapes demonstrated its reliability and generality; the maximum and average deviations for the earing profile were 4.3 % and 3.1 %, respectively, and for post-forming sheet thickness, they were 6.7 % and 3.1 %, respectively.
Keyword:
Deep drawing
Finite element analysis
Machine learning
Gaussian process regression
Parametric design
Blank design mapping function
期刊
IF:
9.4
论文数:
1.0W
被引数:
4.5W
机构
引用论文
Blank optimization in a stamping process-Influence of the geometry definition冲压过程中的毛坯优化-几何定义的影响
PREAI
分享收藏Neural Network-Based Multi-Objective Optimization of Adjustable Drawbead Movement for Deep Drawing of Tailor-Welded Blanks.png)
MATERIALS
OAAI
MATERIALSIF3.2
OAAI分享收藏
查看更多内容