A comparative assessment of artificial neural network and regression models to predict mechanical properties of continuously cooled low carbon steels: an external data analysis approach

Year 2024, Volume: 4 Issue: 2, 495 - 513, 31.07.2024

Emre Alan , İsmail İrfan Ayhan , Bilgehan Ögel Deniz Uzunsoy

https://doi.org/10.61112/jiens.1445518

Abstract

In this study, mechanical properties of continuously cooled low carbon steels were predicted via Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models. Unlike the previous studies, laboratory scaled self-generated data that consists of chemical compositions and cooling rates were used as input while yield strength (YS), ultimate tensile strength (UTS) and total elongation (TE) were served as target data. The prediction performances of the models were compared by applying new data set extracted from external sources like previously studied research papers, thesis or dissertations. A better agreement between predicted and actual data was achieved with ANN model. Additionally, the response of ANN model to new external data resulted in lower prediction errors even the data has one or more input value that is not included in the range of training data set. Unlike ANN model, MLR model shows a significant decrease in prediction accuracy when input data has non-uniform distribution or target data takes place in relatively narrow range. In general, it was shown that ANN model trained with self-generated data can be used as an efficient tool to estimate mechanical properties of continuously cooled low carbon steels that are produced with various conditions, even for the phenomena between input and output is complex and data distribution is non-uniform.

Keywords

Artificial neural network, Multiple linear regression, Continuous cooling, Low carbon steel, Mechanical properties.

Supporting Institution

ÇEMTAŞ Çelik Mak. San. ve Tic. A. Ş., Bursa Technical University

Project Number

TÜBİTAK 1002-A Project No.: 222M041

Sürekli soğutma ile üretilen düşük karbonlu çeliklerin mekanik özelliklerinin tahmininde yapay sinir ağları ve çoklu regresyon analizi karşılaştırması: dış veri doğrulaması yaklaşımı

Abstract

Bu çalışmada, sürekli soğutma ile üretilen düşük karbonlu çeliklerin mekanik özellikleri Yapay Sinir Ağları (ANN) ve Çoklu Regresyon Analizi (MLR) modelleri kullanılarak tahmin edilmiştir. Oluşturulan modellerde çelik kimyasal kompozisyonları ve soğutma hızları (CR) girdi parametreleri olarak kullanılıp akma mukavemetleri (YS), çekme mukavemetleri (UTS) ve uzama değerleri (TEl) hesaplanmıştır. Önceki çalışmalardan farklı olarak, kullanılan parametrelerin tamamı laboratuvar ölçekli üretimlerden elde edilmiş ve modellerin performansları harici olarak farklı kaynaklarda yer alan veriler ile karşılaştırmalı değerlendirilmiştir. Yapılan incelemede ANN modeli ile tahmin edilen değerlerin MLR modeline göre daha düşük hata oranlarında gerçekleştiği gözlemlenmiştir. Ayrıca, dış kaynaklardan alınan veriler ile yapılan tahminlerde ANN modelinin veri setinin düzensiz olduğu durumlarda bile daha yüksek performans sağladığı hesaplanmıştır. MLR modelinde ise kullanılan verilerin düzgün dağılım göstermeyip belirli bir aralıkta kümelendiği veya girdi veri setinin dışında yer aldığı durumlarda performansında önemli bir düşüş oluştuğu belirlenmiştir. Genel olarak değerlendirildiğinde, laboratuvar ölçekli olarak üretilen ve sürekli soğutma uygulanan düşük karbonlu çeliklerin mekanik özelliklerinin tahmini için yapılan karşılaştırmalı incelemede, kullanılan veri setinin düzensiz olduğu durumlarda dahi ANN modeline ait tahminlerin MLR modeline göre daha düşük hata oranlarında elde edildiği belirlenmiştir.

Keywords

Yapay Sinir Ağları, Çoklu Regresyon Analizi, Sürekli Soğutma, Düşük Karbonlu Çelikler, Çeliklerin Mekanik Özellikleri

Project Number

TÜBİTAK 1002-A Project No.: 222M041

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