Assessment of Laser Joining Quality by Visual Inspection, Computer Simulation, and Deep Learning
| Autor: | Hae Woon Choi, Tae Won Kim, Chang Min Han |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Materials science Yield (engineering) Acoustics Overheating (economics) 02 engineering and technology Welding lcsh:Technology law.invention lcsh:Chemistry 020901 industrial engineering & automation law computer simulation General Materials Science lcsh:QH301-705.5 Instrumentation Fluid Flow and Transfer Processes Fusion weld quality assessment lcsh:T Process Chemistry and Technology General Engineering deep learning Laser beam welding 021001 nanoscience & nanotechnology Laser lcsh:QC1-999 Computer Science Applications Visual inspection lcsh:Biology (General) lcsh:QD1-999 polymer joining lcsh:TA1-2040 laser welding Fuse (electrical) lcsh:Engineering (General). Civil engineering (General) 0210 nano-technology lcsh:Physics |
| Zdroj: | Applied Sciences, Vol 11, Iss 642, p 642 (2021) Applied Sciences Volume 11 Issue 2 |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app11020642 |
| Popis: | 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&ndash 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. |
| Databáze: | OpenAIRE |
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