Nanokompozitlerin Delme Prosesinde Delaminasyon ve İtme Kuvvetinin Optimizasyonu

Year 2021, Issue: 32, 807 - 815, 31.12.2021

Nilay Küçükdoğan Öztürk

https://doi.org/10.31590/ejosat.1040182

Abstract

Grafenoksit nano-kompozitlerin delme işleminin analitik performansını geliştirmek için yeni bir tasarım optimizasyon tekniği sunulmuştur. Bu amaçla çoklu doğrusal olmayan nöro-regresyon analizleri kullanılarak delme sürecinin modelleme-tasarım-optimizasyonu için detaylı bir çalışma yapılmıştır. Veriler bu amaç için bir literatür çalışmasından seçilmiştir. Verileri modellemek için sunulan dokuz potansiyel fonksiyonel yapının tahminlerinin doğruluğu, hibrit nöro-regresyon tabanlı bir teknik kullanılarak test edilmiştir. Amaç fonksiyonlarını belirlemek için yapılan model seçimleri sırasıyla R2 değerleri, sınır değerleri ve istatistiksel sonuçlar kontrol edilerek yapılmıştır. Seçilen modeller dört farklı optimizasyon algoritması ile delaminasyon ve itme kuvveti değerlerinin optimizasyon çalışmalarında kullanılmıştır. Sonuçlar, R2eğitim ve R2 eğitim-ayarlanmış değerlerinin amaç fonksiyonu olarak dokuz modelde iyi sonuçlar verdiğini göstermiştir. Ancak, R2test değerleri ve istatistiksel hesaplamalar tüm modeller arasında ayırt edici olmuştur. Ayrıca her iki çıktı için üçüncü dereceden polinom ve logaritmik modellerin optimizasyon sonuçları referans çalışmanın sonuçlarıyla karşılaştırıldığında, mevcut sonuçların test sonuçlarına daha yakın olduğu görülmüştür.

Keywords

Nano kompozitler, Delme prosesi, Nöro-regresyon modellemesi, Optimizasyon

Optimization of Delamination and Thrust Force in the Drilling Process of Nanocomposites

Abstract

A new design optimization technique is presented to improve the analytical performance of the drilling process of graphene oxide nano-composites. A detailed study was conducted for modeling-design-optimization of the drilling process using multiple nonlinear neuro-regression analyses for this goal. The data were slected from a literature study for this objective. The accuracy of the predictions of the nine potential functional structures presented for modeling the data was tested using a hybrid neuro-regression-based technique. Model selections to determine the objective functions were made by controlling the R2 values, limit values, and statistical results, respectively. The selected models were used in the optimization studies of delamination and thrust force values with four different optimization algorithms. The results show that the R2training and R2 training-adjust values give good results in the nine models as objective functions. However, R2testing values and statistical calculations were distinctive among all models. Furthermore, when the optimization results of the third-order polynomial and logarithmic models for both responses were compared to the reference study's results, it was observed that the current results were more closer to the test results.

Keywords

Nano-composites, Drilling prosess, Neuro-regression modeling, Optimization

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