Performing Post-Production Defect Detection of Pet Preforms With Image Processing Technology

Abstract

With the speed and cost advantage provided by the developing technology, image processing technology has started to become widespread especially in the production sector. In this study, the defects in pet preform production were examined and in this direction, it was aimed to eliminate the errors by using an image processing system. Preforms found to be faulty by image processing are distinguished with great precision. For this purpose, the design and manufacture of the mechanical test station was made. In this test device, preforms can be adjusted according to product dimensions, products that pass the test can be moved to the stock box. In this study, features including statistical, textural and morphological features of the patterns obtained after segmentation of the separated product image were determined. It has been proven that the preforms show classification success with 97% accuracy in terms of classification precision. Based on Tables 1 and 2, an enterprise producing 30 million units monthly experienced a loss of 8.5 million units due to adverse conditions. This resulted in significant inefficiencies in energy use, labor, raw materials, and machinery. To address these issues, an image processing test device was developed and implemented in 2021.

Keywords

• Keywords: Image processing
• Test device
• Preform Defects

Görüntü işleme Teknolojisi ile Pet Preformlarının Üretim Sonrası Kusur Tespitinin Yapılması

Abstract

Gelişen teknolojinin sağladığı hız ve maliyet avantajı sayesinde, görüntü işleme teknolojisi özellikle üretim sektöründe yaygınlaşmaya başlamıştır. Bu çalışmada, PET preform üretimindeki kusurlar incelenmiş ve bu doğrultuda, görüntü işleme sistemi kullanılarak hataların giderilmesi amaçlanmıştır. Görüntü işleme ile hatalı olduğu tespit edilen preformlar yüksek hassasiyetle ayrılabilmektedir. Bu amaçla, mekanik test istasyonunun tasarımı ve üretimi gerçekleştirilmiştir. Bu test cihazında, preformlar ürün boyutlarına göre ayarlanabilir ve testi geçen ürünler stok kutusuna yönlendirilebilir. Çalışmada, ayrıştırılan ürün görüntüsünün bölütlenmesinden sonra elde edilen desenlerin istatistiksel, dokusal ve morforlojik özellikleri belirlenmiştir. Preformların sınıflandırma doğruluğu açısından %97 başarı oranı ile sınıflandırma gerçekleştirdiği kanıtlanmıştır. Tablo 1 ve 2'ye göre, aylık 30 milyon birim üretim yapan bir işletmede olumsuz koşullar nedeniyle 8,5 milyon birim kayıp yaşanmıştır. Bu durum, enerji kullanımı, iş gücü, hammadde ve makinelerde ciddi verimsizliklere yol açmıştır. Bu sorunları gidermek amacıyla 2021 yılında bir görüntü işleme test cihazı geliştirilip uygulanmıştır.

Keywords

• Görüntü işleme
• Test cihazı
• Preform kusurları

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