Artificial neural network and multiple regression analysis for predicting abrasive water jet cutting of al 7068 aerospace alloy
Yazarlar (2)
Prof. Dr. Fuat KARTAL Kastamonu Üniversitesi, Türkiye
Dr. Öğr. Üyesi Arslan Kaptan Sivas Cumhuriyet Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(ESCI dergilerinde yayınlanan tam makale)
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| Dergi Adı | Sigma Journal of Engineering and Natural Sciences | ||
| Dergi ISSN | 1304-7191 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | mühendislik | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2024 |
| Cilt / Sayı / Sayfa | 42 / 2 / 516–528 | DOI | 10.14744/sigma.2023.00102 |
| Makale Linki | https://doi.org/10.14744/sigma.2023.00102 | ||
| UAK Araştırma Alanları |
Üretim Teknolojileri
Optimizasyon ve Teknikleri
Eklemeli İmalat
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| Özet |
| This study aims to predict machinability and high performance optimum surface roughness (Ra) by developing multiple regression models and artificial neural network (ANN) model for abrasive water jet cutting (AWJC) of Aluminum 7068 alloy. Important basic processing parameters such as pump pressure (3500-4000 Bar), nozzle distance (2-5 mm), abrasive flow rate (200-350 g/min), abrasive grain size (100-110 mesh), and nozzle traverse speed (240-300 mm/min) were selected in the study. To examine the effects of these parameters on Ra, 32 experiments were conducted using the L32 orthogonal array, and data was collected. Ad-ditionally, the most important factors and interactions affecting Ra were determined using multiple regression analysis and analysis of variance (ANOVA). The Artificial Neural Network (ANN) model was designed to have multiple hidden layers using MATLAB. The model was trained and evaluated using experimental data, and its performance was measured using mean squared error (MSE) and mean absolute error (MAE). The model was optimized using hyper parameter tuning and cross-validation techniques. As a result, it was determined that the best R2 value of 95.65% from the multiple regression models created to estimate the surface rough-ness could be obtained from the linear regression model. While selecting the optimum process parameters for AWJC, it was determined that nozzle rotation speed, abrasive grain size and flow rate had the greatest effect by 35.5%, 25.4% and 21.9%, respectively. The optimized ANN model showed high accuracy in predicting Ra for different input parameter combinations … |
| Anahtar Kelimeler |
| Abrasive Water Jet Cutting | Aerospace Alloy | Al 7068 | ANOVA | Artificial Neural Network | Surface Roughness |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 4 |
| Google Scholar | 7 |