{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T01:55:29Z","timestamp":1775094929781,"version":"3.50.1"},"reference-count":20,"publisher":"Walter de Gruyter GmbH","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,29]]},"abstract":"<jats:title>Zusammenfassung<\/jats:title>\n                  <jats:p>Die Qualit\u00e4tssicherung ist ein \u00fcberaus zentrales Thema in der Fertigungsindustrie, da sie unmittelbar mit der Produktqualit\u00e4t und der Kundenzufriedenheit zusammenh\u00e4ngt. Fortschritte in Algorithmen und modernen Kommunikationstechnologien im Kontext von Industrie 4.0 haben dazu beigetragen, dass traditionelle Fertigungsindustrien Deep-Learning-Modelle zur Kontrolle der Produktionsqualit\u00e4t einsetzen. Allerdings stellen industrielle Anwendungen hohe Anforderungen an die Effizienz von Algorithmen. Zudem fehlen in praktischen Anwendungen h\u00e4ufig umfangreiche, gelabelte Daten f\u00fcr das Training von Deep-Learning-Modellen. Um diesen Herausforderungen zu begegnen, haben wir in diesem Artikel ein auf maschinellem Lernen basierendes Modell zur Qualit\u00e4tserkennung entwickelt. Unser Modell nutzt eine effizientere Hesse-Matrix-Erkennungsmethode, um direkt die lokalen Maxima im Skalenraum des Eingangsbildes zu identifizieren, ohne zahlreiche Gauss-Differenzbilder berechnen zu m\u00fcssen. Dar\u00fcber hinaus wenden wir Methoden der Bildverarbeitung an, um die Trainingsdaten zu erweitern, sodass das Modell auch bei geringen Trainingsdatenmengen eine hohe Genauigkeit erreicht. Unsere experimentellen Ergebnisse zeigen, dass das vorgeschlagene Modell die h\u00f6chste Genauigkeit und Effizienz im Vergleich zu g\u00e4ngigen Methoden aufweist. Abschlie\u00dfend haben wir in diesem Artikel auch eine benutzerfreundliche Schnittstelle f\u00fcr unser Modell erstellt und dieses in das elektronische Kanban der Werkstatt integriert. Unsere empirischen Studien haben ergeben, dass die entwickelten Systeme in der industriellen Praxis anwendbar sind und die Fehlerquote senken sowie die Produktqualit\u00e4t erh\u00f6hen k\u00f6nnen.<\/jats:p>","DOI":"10.1515\/auto-2024-0004","type":"journal-article","created":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T02:22:19Z","timestamp":1728958939000},"page":"61-80","source":"Crossref","is-referenced-by-count":0,"title":["Hesse-Matrix-basierte Qualit\u00e4tsmanagementsysteme f\u00fcr die Fertigungsindustrie"],"prefix":"10.1515","volume":"73","author":[{"given":"Peng","family":"Jieyang","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering , Tsinghua University , Beijing 100084 , P.R. China"},{"name":"Institut f\u00fcr Informationsmanagement im Ingenieurwesen (IMI), Karlsruhe Institute of Technology , 76133 Karlsruhe , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wang","family":"Dongkun","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering , Tsinghua University , Beijing 100084 , P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Kimmig","sequence":"additional","affiliation":[{"name":"Institut f\u00fcr Informationsmanagement im Ingenieurwesen (IMI), Karlsruhe Institute of Technology , 76133 Karlsruhe , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Siemens AG , Berlin , German"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Armin","family":"Roux","sequence":"additional","affiliation":[{"name":"Siemens AG , Berlin , German"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jivka","family":"Ovtcharova","sequence":"additional","affiliation":[{"name":"Institut f\u00fcr Informationsmanagement im Ingenieurwesen (IMI), Karlsruhe Institute of Technology , 76133 Karlsruhe , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2024,10,15]]},"reference":[{"key":"2026040201304655081_j_auto-2024-0004_ref_001","unstructured":"S. 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