Formability classifier for a TV back panel part with machine learning

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.

Automated summary

This study proposes a machine learning-based methodology for evaluating the formability of sheet metals. The XGBoost classifier is developed to classify the formability of the TV back panel, and the trained model maps the panel geometry to specific formability classes. Additionally, the study demonstrates the improvement of formability for a new design through the optimization of the punch radius.