{"id":88759,"date":"2025-02-21T11:29:46","date_gmt":"2025-02-21T03:29:46","guid":{"rendered":"https:\/\/solomon-3d.mesvr.tw\/?p=88759"},"modified":"2025-02-21T11:32:20","modified_gmt":"2025-02-21T03:32:20","slug":"metallbolzenfehlererkennung-mit-ki","status":"publish","type":"post","link":"https:\/\/solomon-3d.mesvr.tw\/de\/anwenderberichte\/metallbolzenfehlererkennung-mit-ki\/","title":{"rendered":"Metallbolzenfehlererkennung mit KI"},"content":{"rendered":"
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<\/span>\"pile
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SolVisionFallstudie<\/span><\/h2>\n\n\n\n
Fertigung<\/a><\/div>\n<\/div><\/div>\n\n\n\n
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Metallbolzenfehlererkennung mit KI<\/h1><\/div><\/div>\n\n\n\n
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Kunde<\/h2>\n\n\n\n

Der Kunde ist ein f\u00fchrender Hersteller von Schrauben und Bolzen mit Hauptsitz in Asien.<\/p>\n<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n

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Fall<\/h2>\n\n\n\n
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Qualit\u00e4tskontrolle von Metallbolzen<\/h3>\n\n\n\n

Der Kunde suchte nach einer pr\u00e4zisen L\u00f6sung zur Fehlererkennung, die in der Lage ist, M\u00e4ngel wie kleine Dellen an hergestellten Bolzen zu identifizieren, unabh\u00e4ngig von der Position oder Orientierung der Bolzen.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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\"pile<\/div>\n<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n
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Herausforderung<\/h2>\n\n\n\n
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Die Reduzierung der Falsch-Negativ-Rate bei der Fehlererkennung<\/h3>\n\n\n\n

Um Redundanz in der Fehlererkennung zu vermeiden, musste jeder Bolzen einzeln aus drei verschiedenen Winkeln analysiert werden. Aufgrund des hohen Produktionsvolumens ben\u00f6tigte der Kunde, dass der Inspektionsprozess innerhalb von 1 Sekunde pro Bolzen abgeschlossen wird, w\u00e4hrend eine Mindestgenauigkeitsrate von 90 % aufrechterhalten werden muss. Diese Aufgabe wurde zus\u00e4tzlich durch die reflektierende Oberfl\u00e4che der Bolzen erschwert, da Lichtreflexionen f\u00e4lschlicherweise als M\u00e4ngel interpretiert werden konnten.<\/p>\n<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n

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L\u00f6sung<\/h2>\n\n\n\n
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Innovative KI-Inspektionsl\u00f6sung<\/h3>\n\n\n\n

Solomon entwickelte ein robotergest\u00fctztes System, das jede Schraube 360 Grad dreht und dabei drei Bilder aufnimmt, um m\u00f6gliche M\u00e4ngel zu identifizieren. Um die Herausforderungen durch die reflektierende Oberfl\u00e4che zu bew\u00e4ltigen, wurde ein KI-Modell in SolVision<\/a> trainiert, und unsere Ingenieure gaben dem Kunden Empfehlungen f\u00fcr optimale Licht- und Hintergrundbedingungen, um Reflexionen zu minimieren. Dieser integrierte Ansatz erm\u00f6glichte einen schnellen Inspektionsprozess, der die Inspektion in nur 49 Millisekunden pro Schraube mit einer fehlerfreien Genauigkeit von 100 % abschloss.<\/p>\n<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n

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Ergebnis<\/h2>\n\n\n\n
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<\/path><\/svg>49 Millisekunden pro Inspektionszyklus pro Schraube<\/span><\/div>
<\/path><\/svg>100% Fehlererkennungsgenauigkeit<\/span><\/div>
<\/path><\/svg>Die falsch-negative Rate auf null reduziert<\/span><\/div><\/div>\n<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n

<\/p>","protected":false},"excerpt":{"rendered":"

SolVision gew\u00e4hrleistet eine fehlerfreie 100 %ige Fehlererkennungsrate in nur 49 Millisekunden pro Bolzen, indem es Herausforderungen durch reflektierende Oberfl\u00e4chen angeht und falsch-negative Ergebnisse ausschlie\u00dft.<\/p>","protected":false},"author":875,"featured_media":67680,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_gspb_post_css":"#gspb_heading-id-gsbp-5f6009ef-2b72{font-size:60px;line-height:100px;text-align:center!important;color:var(--gs-colorone);margin-top:50px}#gspb_heading-id-gsbp-5f6009ef-2b72 .gsap-g-line{text-align:center!important}#gspb_heading-id-gsbp-5f6009ef-2b72 .gspb_heading_subtitle,#gspb_subheading-id-gsbp-5f6009ef-2b72{display:block;line-height:22px;margin-top:5px;font-size:30px;color:var(--wp--preset--color--white, #ffffff)}.gspb_text-id-gsbp-fa6ec577-bc74{font-size:20px;text-align:center!important;font-weight:500!important}.gspb_text-id-gsbp-fa6ec577-bc74 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