{"id":88272,"date":"2025-02-20T11:52:22","date_gmt":"2025-02-20T03:52:22","guid":{"rendered":"https:\/\/solomon-3d.mesvr.tw\/?p=88272"},"modified":"2025-02-20T11:53:10","modified_gmt":"2025-02-20T03:53:10","slug":"ai-gestuetzte-fehlererkennung-bei-schweissnaehte","status":"publish","type":"post","link":"https:\/\/solomon-3d.mesvr.tw\/de\/referenzen\/ai-gestuetzte-fehlererkennung-bei-schweissnaehte\/","title":{"rendered":"AI-gest\u00fctzte Fehlererkennung bei Schwei\u00dfn\u00e4hte"},"content":{"rendered":"
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SolVisionFallstudie<\/span><\/h2>\n\n\n\n
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Fertigung<\/a><\/span><\/p>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n

AI-gest\u00fctzte Fehlererkennung bei Schwei\u00dfn\u00e4hte<\/h1><\/div><\/div>\n\n\n\n

Qualit\u00e4tskontrolle beim Schwei\u00dfen von Produkten f\u00fcr Klimaanlagen und K\u00fchlung<\/strong><\/h2>\n\n\n\n
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Automatisierung der Schwei\u00dfnahtinspektion<\/h2>\n<\/div>\n\n\n\n
Bei der Herstellung von Klimaanlagen und K\u00fchlschr\u00e4nken m\u00fcssen die Komponenten, aus denen der W\u00e4rmetauscher besteht, wie Rohre, Spiegelplatten sowie vordere und hintere Sammler, geschwei\u00dft werden. Um die Schwei\u00dfqualit\u00e4t dieser Teile zu \u00fcberpr\u00fcfen, ist ein Inspektionsprozess erforderlich.<\/div>\n<\/div>\n\n\n\n
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<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n
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Unregelm\u00e4\u00dfig geformte Schwei\u00dffehler<\/strong><\/strong><\/h2>\n<\/div>\n\n\n\n
Schwei\u00dffabriken sind oft Hochtemperatur- und Hochhitzeanlagen, und die Arbeitsbedingungen sind gef\u00e4hrlich. Schwei\u00dffehler sind zudem h\u00e4ufig komplex und unregelm\u00e4\u00dfig, sodass eine manuelle Inspektion m\u00f6glicherweise nicht die beste L\u00f6sung ist, um die Durchsatzrate zu maximieren oder eine konstante Produktion sicherzustellen.<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n
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AI-Fehlererkennung mit SolVision<\/strong><\/strong><\/strong><\/strong><\/h2>\n<\/div>\n\n\n\n
Angetrieben von KI kann SolVision<\/a> die Schwei\u00dfinspektionsprozesse automatisieren, indem es die verschiedenen Formen und Merkmale von Schwei\u00dfn\u00e4hten aus Beispielbildern lernt und die KI dann darauf trainiert, schnell zu erkennen, ob eine Naht fehlerhaft ist, basierend auf ihren Merkmalen, z. B. ob sie zu d\u00fcnn, zu dick oder ganz fehlt. Durch die Erkennung fehlerhafter N\u00e4hte wird die Qualit\u00e4t des Schwei\u00dfprozesses effektiv kontrolliert.<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n
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Beispiel<\/strong><\/h2>\n<\/div>\n\n\n\n
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Vollst\u00e4ndiger Schwei\u00dfnaht<\/strong><\/p>\n\n\n

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\"Welding<\/figure>\n<\/div><\/div>\n<\/div>\n\n\n\n
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Fehlende Schwei\u00dfnaht<\/strong><\/p>\n<\/div>\n\n\n

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\"Welding<\/figure>\n<\/div><\/div>\n\n\n\n
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Unvollst\u00e4ndige Schwei\u00dfung<\/strong><\/p>\n<\/div>\n\n\n

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\"Welding<\/figure>\n<\/div><\/div>\n <\/div><\/div>\n<\/div>\n <\/div><\/div>","protected":false},"excerpt":{"rendered":"

Angetrieben von KI kann Solomon SolVision die Schwei\u00dfinspektionsprozesse automatisieren, indem es die verschiedenen Formen und Merkmale von Schwei\u00dfn\u00e4hten aus Beispielbildern erlernt.<\/p>","protected":false},"author":875,"featured_media":56373,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_gspb_post_css":"#gspb_heading-id-gsbp-d51e703b-ff95{font-size:60px;line-height:100px;text-align:center!important;color:var(--gs-colorone);margin-top:15px;margin-bottom:15px}#gspb_heading-id-gsbp-d51e703b-ff95 .gsap-g-line{text-align:center!important}#gspb_heading-id-gsbp-d51e703b-ff95 .gspb_heading_subtitle,#gspb_subheading-id-gsbp-d51e703b-ff95{display:block;line-height:22px;margin-top:5px;font-size:30px;color:var(--wp--preset--color--white, 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