Bulanık Mantık ve Görüntü İşleme ile Metal Döküm Ürünlerinin Otomatik Kalite Derecelendirmesi

Abstract

Yüksek hacimli üretim süreçlerinde kalite kontrol, özellikle manuel denetimlerin hataya açık ve tutarsız olması nedeniyle önemli bir zorluktur. Bu çalışmada, kusur tespiti ve sınıflandırma süreçlerini iyileştirmek amacıyla görüntü işleme ve bulanık mantık tekniklerini entegre eden otomatik bir kalite değerlendirme sistemi önerilmiştir. Çalışmada, 512×512 piksel boyutlarında 1.300 adet disk biçimli döküm ürünü içeren bir veri seti kullanılmıştır. Görüntü işleme aşamasında Gauss filtreleme ve Canny kenar algılama teknikleriyle ürünlerin kusur boyutu, renk farkı ve şekil bozulması gibi özellikleri elde edilmiştir. Bu veriler, bulanık mantık tabanlı bir sistemle işlenerek ürünlere kalite puanı atanmış ve ürünler düşük (≤40), orta (41–70) ve yüksek (>70) kalite olarak sınıflandırılmıştır. Sonuçlara göre ürünlerin %84,8’i düşük kalite, sadece %3,6’sı ise yüksek kalite sınıfına girmiştir. Buna ek olarak, kalite puanlarına dayalı sıralama için Bulanık TOPSIS yöntemi uygulanarak, ürünlerin ideal çözüme yakınlığına göre önceliklendirilmesi sağlanmıştır. Bu hibrit yapı, kalite değerlendirmesinde tutarlılığı artırmakta ve karar destek sistemlerine entegrasyon imkânı sunmaktadır.

Keywords

• Bulanık Mantık
• Görüntü İşleme
• Kusur Tespiti
• Bulanık TOPSIS
• Kalite Sıralaması
• Döküm Ürünleri
• Çok Kriterli Karar Verme

Integrated Fuzzy Logic and Image Processing for Automated Quality Grading in Metal Casting

Abstract

Improving quality control in high-volume manufacturing remains a critical challenge, especially where manual inspections are error-prone and inconsistent. This study presents an automated quality assessment framework combining image processing and fuzzy logic techniques to enhance defect detection and classification. A dataset comprising 1,300 grayscale images of disk-shaped cast products (512×512 pixels) was analyzed. Gaussian filtering and Canny edge detection were applied to extract defect features such as size, color deviation, and shape distortion. These features were processed through a fuzzy inference system to assign quality scores, categorizing products into low (≤40), medium (41–70), and high quality (>70). Results revealed that 84.8% of products were low quality, while only 3.6% were rated high quality. Additionally, the Fuzzy TOPSIS method was implemented to rank products based on similarity to an ideal solution, offering a decision-support perspective beyond classification. This hybrid approach improves consistency, objectivity, and scalability in quality evaluation workflows.

Keywords

• Fuzzy Logic
• Image Processing
• Defect Detection
• Fuzzy TOPSIS
• Quality Ranking
• Cast Products
• Multi-Criteria Decision Making

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