Fatigue Life Prediction of 6082 Aluminum Alloy with Machine Learning

Abstract

Aluminum alloys are one of the most preferred materials in industry. One of the most important properties of aluminum is its low density. In this way, it has taken its place as an important engineering material in many sectors, including the automotive sector. One of the aluminum alloys used in the automotive industry is the 6082 series aluminum alloy. Machine parts subjected to repetitive loads develop and accumulate micro cracks over time and these cracks cause sudden breakage. It is of great importance to understand this phenomenon known as fatigue in materials and to determine the number of fracture cycles by performing fatigue tests. In this study, during the fatigue tests of 6082 aluminum alloy, current and voltage values were collected from the specimen using DCPD (Direct Current Potential Drop) technique, while the applied force and displacement data were recorded simultaneously. These data were then given as input to 5 different machine learning algorithms: decision trees, extra trees, random forest, XGBoost and KNN (K-Nearest Neighbor) and the number of cycles were estimated. Among the tested models, the decision trees machine learning model performed the best based on R-square (R2) and mean absolute percentage error (MAPE) values.

Keywords

• Fatigue
• DCPD
• 6082 Aluminum alloy
• Machine learning
• Prediction

Makine Öğrenmesi ile 6082 Alüminyum Alaşımının Yorulma Ömrü Tahmini

Öz

Alüminyum alaşımlar endüstride oldukça sık tercih edilen malzemelerdendir. Alüminyumun en önemli özelliklerinden biri yoğunluklarının düşük olmasıdır. Bu sayede otomotiv sektörü de dahil olmak üzere birçok sektörde önemli bir mühendislik malzemesi olarak yerini almıştır. Otomotiv sektöründe kullanılan alüminyum alaşımlardan bir tanesi de 6082 serisi alüminyum alaşımıdır. Tekrarlı yüklere maruz kalan makine parçalarında zamanla mikro çatlaklar oluşarak birikir ve bu çatlaklar ani kırılmalara sebep olur. Malzemelerde yorulma olarak bilinen bu olgunun anlaşılması ve yorulma deneylerinin yapılarak kırılma çevrim sayılarının belirlenmesi büyük önem taşır. Bu çalışmada 6082 alüminyum alaşımının yorulma testleri sırasında DCPD (Direct Current Potential Drop) tekniğiyle numune üzerinden akım ve gerilim değerleri toplanırken, uygulanan kuvvet ve meydana gelen deplasman verileri de eş zamanlı olarak kaydedilmiştir. Daha sonra bu veriler karar ağacı, ekstra ağaçlar, rastgele orman, XGBoost (Aşırı Gradyan Arttırma) ve KNN (K-En Yakın Komşu) olmak üzere 5 farklı makine öğrenmesi algoritmasına girdi olarak verilmiş ve çevrim sayıları tahmin edilmiştir. Test edilen modeller arasında R-kare (R2) ve ortalama mutlak yüzde hata (MAPE) değerleri baz alındığında en iyi performansı karar ağacı ve ekstra ağaçlar makine öğrenmesi modelleri göstermiştir.

Anahtar Kelimeler

• Yorulma
• DCPD
• 6082 Alüminyum alaşımı
• Makine öğrenmesi
• Tahmin

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