Prediction of Failure Categories in Plastic Extrusion Process with Deep Learning

Yıl 2022, Cilt: 5 Sayı: 1, 27 - 34, 02.03.2022

Fatma Demircan Keskin , Ural Çiçekli , Doğukan İçli

https://doi.org/10.38016/jista.878854

Cited By: 1

Öz

Today’s manufacturing vision necessitates extracting insights from the data collected in real-time from manufacturing processes. Predicting failures with the predictive analysis of the collected process data and preventing these failures by taking necessary actions before they occur is a key factor in ensuring quality at the desired level, increasing productivity, and reducing costs in production systems. In the literature on predictive analysis of process data, machine learning and deep learning methods have attracted considerable attention, especially in recent years. This study has addressed a multi-class failure classification problem in the plastic extrusion process with a real case study. Classification models have been developed based on Long Short-term Memory (LSTM) as a deep learning method and Multilayer Perceptron (MLP) and Logistic Regression (LR) as machine learning methods to predict the failure categories. In the case study, real data taken from the extrusion process of one of the leading insulation companies operated in Izmir has been used. The final dataset includes actual measurements of seven parameters related to temperature and pressure and failure categories as the target variable. Three failure categories have been identified to define Category 0 (No failure), Category 1 (Filter change), and Category 2 (Feeding failures) states, and coded as 0,1 and 2 in the models, respectively. LSTM, MLP, and LR’s performance to predict the failure categories have been evaluated and compared based on accuracy, precision, recall, and F1 Score measures. LSTM is the highest performing among the three methods, with 100% prediction accuracy for each failure category. On the other hand, LR and MLP have achieved considerable and close results except for Category 1.

Anahtar Kelimeler

Deep learning, failure prediction, machine learning, plastic extrusion process

Plastik Ekstrüzyon Sürecinde Derin Öğrenme İle Hata Kategorilerinin Tahmini

Öz

Günümüz üretim anlayışı, imalat süreçlerinden gerçek zamanlı olarak toplanan süreç verisinden kestirim yapabilmeyi gerektirmektedir. Toplanan süreç verilerinin kestirimci analizi ile hataların tahmin edilmesi ve gerekli aksiyonların alınmasıyla hataların ortaya çıkmadan önlenmesi, üretim sistemlerinde kalitenin istenilen seviyede sağlanması, verimliliğin artırılması ve maliyetlerin azaltılmasında kilit bir faktördür. Makine öğrenmesi ve derin öğrenme yöntemleri, süreç verilerinin kestirimci analizinde, özellikle son dönemlerde büyük ilgi görmektedir. Bu çalışmada plastik ekstrüzyon sürecinde çok sınıflı hata sınıflandırma problemi bir gerçek hayat örneğiyle ele alınmıştır. Problemin çözümü için derin öğrenme yöntemlerinden Uzun-Kısa Süreli Bellek (LSTM) ve makine öğrenmesi yöntemlerinden Çok Katmanlı Algılayıcı (MLP) ve Lojistik Regresyon (LR) kullanılmıştır. Çalışmanın uygulama kısmında, İzmir'de faaliyet gösteren Türkiye'nin önde gelen yalıtım firmalarından birinin plastik ekstrüzyon sürecinden alınan gerçek veriler kullanılmıştır. Nihai veri seti, süreçten alınan sıcaklık ve basınçla ilişkili yedi parametrenin gerçek ölçümlerini ve hedef değişken olarak hata kategorilerini içermektedir. Modellerde Kategori 0 (Hata yok), Kategori 1 (Filtre değişimi) ve Kategori 2 (Besleme hataları) durumlarını tanımlamak için üç hata kategorisi belirlenmiş ve sırasıyla 0,1 ve 2 olarak kodlanmıştır. LSTM, MLP ve LR'nin hata kategorilerini tahmin etme performansı, tahmin doğruluğu, kesinlik, duyarlılık ve F1 skoru metriklerine göre değerlendirilmiş ve karşılaştırılmıştır. LSTM, her hata kategorisi için %100 tahmin doğruluğu ile en yüksek performansa sahip olmuştur. LR ve MLP, Kategori 1 dışındaki hata kategorileri tahminlerinde başarılı ve birbirine yakın sonuçlar elde etmiştir.

Anahtar Kelimeler

Derin öğrenme, Hata tahmini, Makine öğrenmesi, plastik ekstrüzyon süreci

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