Assessment of Laser Joining Quality by Visual Inspection, Computer Simulation, and Deep Learning

Abstract

Polymer joining results are evaluated and compared in different ways, such as visual inspection, computer simulation, and deep learning analysis, to assess the joining quality. For the experiments, energies in the range of 3 to 5 J/mm were used from preliminary experimental data. A total of 15 welding experiment schedules were performed. Weld defects due to a lack of fusion were detected in some regions of specimens treated with a low-power laser region (3 J/mm), where a lack of fusion, in turn, occurred due to underheating. Bubble-shaped weld defects were observed in some specimens treated with a high-power laser region (5 J/mm); melting occurred due to the overheating of the specimen. Computer simulations were used to trace the boundaries of the fusion zone, and yielded results similar to the visual inspection ones. In the lower-energy region, the energy may not be sufficient to fuse the specimen, whereas the high-energy region may have sufficient energy to break down the polymer chains. A novel deep learning algorithm was used to statistically evaluate the weld quality. Approximately 1700–1900 samples were collected for each condition, and the pre-trained quality evaluation indicated a highly reliable (>98%) welding classification (fail or good). According to the results of this study, welding quality assessments based on visual inspection, computer simulation, and DL-based inspection yield similar results.