The use of artificial neural network for prediction of grain size of 17 4 pH stainless steel powders
Yazarlar (3)
Doç. Dr. Tayfun Fındık Gazi Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Selçuk Üniversitesi, Türkiye
Prof. Dr. İsmail Şahin Gazi Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Scientific Research and Essays (Q3) | ||
| Dergi ISSN | 1992-2248 Dergi Bilgileri (2010) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 06-2010 |
| Cilt / Sayı / Sayfa | 5 / 11 / 1274–1283 | DOI | – |
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| This study is aimed to deals with artificial neural network (ANN) approach for prediction grain size (GS) of 17 - 4 pH stainless steel powders. Experimental data which were obtained from experimental studies in a laboratory environment have been used for this modeling. Using some of the experimental data for training and testing an ANN for GS was developed. In these systems, output parameters GS has been determined using input parameters including environment, time, speed, ball diameter, ball ratio, and material. When experimental data and results obtained from ANN were compared by regression analysis in Matlab, it was determined that both groups of data are consistent. The correlation coefficient between estimated GS values and experimental data obtained are 0.99 for traing and 0.98 for testing respectively. The correlation coefficient is closely to 1. This coefficient shows that there is a strong … |
| Anahtar Kelimeler |
| Artificial neural network | Garin size | Mechanical milling |
