Machine Learning for Camera-Based Monitoring of Laser Welding Processes
Overview & Synopsis
The increasing use of automated laser welding processes causes high demands on process monitoring. This work demonstrates methods that use a camera mounted on the focussing optics to perform pre-, in-, and post-process monitoring of welding processes. The implementation uses machine learning methods. All algorithms consider the integration into industrial processes. These challenges include a small database, limited industrial manufacturing inference hardware, and user acceptance.
Indexed from repository: OAPEN Library
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www.oapen.org)
Document Information
| Document Title | Machine Learning for Camera-Based Monitoring of Laser Welding Processes |
|---|---|
| Author / Organization | Hartung, Julia |
| Subject & Discipline | CNN; stacked dilated U-Net; semantic segmentation; hairpin technology; laser welding; quality assurance; machine learning; Qualitätssicherung; semantische Segmentierung; Hairpin Technologie; Laserschweißen; Maschinelles Lernen; Künstliche Intelligenz |
| Categories | Humanities & Social Sciences |
| Publication Year | 2024 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
OAPEN Library
(www.oapen.org)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-71024 |
| Original Source Link | Visit Source Page on www.oapen.org » |