This study proposes a machine learning-based methodology for evaluating the formability of sheet metals. An XGBoost (eXtreme Gradient Boosting) machine learning classifier is developed to classify the formability of the TV back panel based on the forming limit curve (FLC). The input to the XGBoost model is the blank thickness and cross-sectional dimensions of the screw holes, AC (Alternating Current), and AV (Audio Visual) terminals on the TV back panel. The training dataset is generated using finite element simulations and verified through experimental strain measurements. The trained classification model maps the panel geometry to one of three formability classes: safe, marginal, and cracked. Strain values below the FLC are classified as safe, those within 5% margin of the FLC are classified as marginal, and those above are classified as cracked. The statistical accuracy and performance of the classifier are quantified using the confusion matrix and multiclass Receiver Operating Characteristic (ROC) curve, respectively. Furthermore, in order to demonstrate the practical viability of the proposed methodology, the punch radius of the screw holes is optimized using Brent's method in a Java environment. Remarkably, the optimization process is completed swiftly, taking only 3.11 s. Hence, the results demonstrate that formability for a new design can be improved based on the predictions of the machine learning model.

本研究提出了一种基于机器学习的方法来评估金属板材的可成形性。开发了 XGBoost（eXtreme Gradient Boosting）机器学习分类器，用于根据成形极限曲线（FLC）对电视背板的成形性进行分类。XGBoost 模型的输入是电视背面板上螺孔、AC（交流）和 AV（视听）端子的毛坯厚度和横截面尺寸。训练数据集是使用有限元模拟生成的，并通过实验应变测量进行验证。经过训练的分类模型将面板几何形状映射到三个可成型性类别之一：安全、边缘和裂纹。低于 FLC 的应变值被分类为安全，在 FLC 5% 范围内的应变值被分类为边缘，而高于 FLC 的应变值被分类为破裂。分类器的统计准确性和性能分别使用混淆矩阵和多类接收者操作特征 (ROC) 曲线进行量化。此外，为了证明所提出方法的实际可行性，在 Java 环境中使用 Brent 方法优化了螺钉孔的冲头半径。值得注意的是，优化过程很快完成，仅用了 3.11 秒。因此，结果表明，新设计的可成型性可以根据机器学习模型的预测得到改善。