# Assessment of Laser Joining Quality by Visual Inspection, Computer Simulation, and Deep Learning
## 4 Conclusions and Discussion  
This paper reports an assessment of laser polymer joining by comparing visual inspection, computer simulation, and DL-based prediction. Based on the results of the preliminary research, these three modalities were used to increase the accuracy of the laser welding quality assessments.  
For the experiments, energies in the range of 3-5 J/mm were used. A total of 15 welding experiments were performed, and the laser power and welding speed were varied. Weld defects due to a lack of fusion were detected in some regions of specimens treated with the low-power laser (3 J/mm), where a lack of fusion occurred due to underheating. Bubble-shaped weld defects were observed in some specimens treated with the high-power laser (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. 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.  
Our results were then reevaluated using a novel DL algorithm which was modified from the AlexNet algorithm. Using a data acquisition (DAQ) system with a CCD camera, image data were acquired every 100 ms with 10 ms of exposure time, and approximately 1700-1900 samples were collected for each condition, with 28,500 images in total were used for training. The pre-trained quality evaluation indicated a highly reliable (>98%) welding classification (fail or good). Specifically, the accuracy of the fail classification was over 99%, and that of the good classification was greater than 97% in the 3 J/mm condition. The accuracy was similar between the 4 and 5 J/mm conditions. Failures were mainly due to pores or bubbles at the joining boundary. However, in some cases light reflected by scratches on the surface led to an incorrect classification as joining failure, which reduced the reliability of our DL algorithm-based quality evaluation.  
According to the results of this study, welding quality assessments based on visual inspection, computer simulation, and DL-based inspection yield similar results. In conclusion, a visual inspection can be replaced with image-based DL and computer simulation for similarly reliable quality inspections but with significant cost and labor savings.  
Conceptualization, C.M.H., T.W.K., and H.W.C.; experiment, C.M.H.; deep learning, T.W.K.; formal analysis, H.W.C.; data curation, T.W.K.; writing--original draft preparation, C.M.H.; writing--review and editing, T.W.K.; supervision, H.W.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Korea Nation Research Foundation, grant no.: NRF-2019R1F1A1062594.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
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The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.