Deep-learning approach for predicting laser-beam absorptance in full-penetration laser keyhole welding

Abstract

Laser-beam absorptance in a keyhole is generally calculated using either a ray-tracing method or electrodynamic simulation, both physics-based. As such, the entire computation must be repeated when the keyhole geometry changes. In this study, a data-based deep-learning model for predicting laser-beam absorptance in full-penetration laser keyhole welding is proposed. The model uses a set of keyhole top- and bottom-aperture as inputs. From these, an artificial intelligence (AI) model is trained to predict the laser-energy absorptance value. For the training dataset, various keyhole geometries (i.e., top- and bottom-aperture shapes) are hypothetically created, upon which the ray-tracing model is employed to compute the corresponding absorptance values. An image classification model, ResNet, is employed as a learning recognizer of features to predict absorptance. For image regression, several modifications are applied to the structure. Five model depths are tested, and the optimal AI architecture is used to predict the absorptance with an R 2 accuracy of 99.76% within 1.66 s for 740 keyhole shapes. Using this model, several keyhole parameters affecting the keyhole absorptance are identified.