Artificial intelligence-assisted laser ultrasound method for the estimation of porosity in hairpin weld seams

Hairpin technology is being used as a replacement for the traditional winding stator in electric motors. In hairpin stator manufacturing, copper rods are used to achieve a higher slot fill factor. These rods are joined together in pairs through laser welding, forming a closed circuit. However, this welding process is prone to air inclusions in the welds, which can negatively impact the efficiency and durability of the motor. The present study aims to estimate the total volume of these air inclusions using laser ultrasonic measurements. Laser ultrasound is a fast, non-contact, non-destructive method that can cope with the limited sample accessibility, making it ideal for inline testing of these weld seams. To evaluate the effectiveness of laser ultrasound, a stator was intentionally manipulated prior to laser welding to favor the formation of air inclusions. The porosity of the weld seams was determined through computed tomography images. It was demonstrated that due to the complex geometry of the hairpin welds, leading to a complex ultrasound wave field, standard methods to estimate the porosity from laser ultrasound B-scans are difficult to apply. As an alternative approach, an algorithm that is based on artificial intelligence was utilized for the purpose of estimating the air inclusion volume in the welds from laser ultrasonic measurements. The outcomes demonstrated a median correlation of 0.6 between this estimate and the pore volume obtained from the computed tomography data, despite the utilization of only 48 samples. Moreover, these results were evaluated against a model where the labels were randomly mixed, and highly informative regions regarding pore volume were identified in the B-scans, which have the potential to accelerate the process of acquiring data.