PREDICTION OF THE WEAR RATES OF EPOXY-BASED NANOCOMPOSITES USING ARTIFICIAL NEURAL NETWORK APPROACH
Al Shatti, K. (2022). PREDICTION OF THE WEAR RATES OF EPOXY-BASED NANOCOMPOSITES USING ARTIFICIAL NEURAL NETWORK APPROACH. EKB Journal Management System, 17(63), 569-576. doi: 10.21608/auej.2022.233841
Khaled Al Shatti. "PREDICTION OF THE WEAR RATES OF EPOXY-BASED NANOCOMPOSITES USING ARTIFICIAL NEURAL NETWORK APPROACH". EKB Journal Management System, 17, 63, 2022, 569-576. doi: 10.21608/auej.2022.233841
Al Shatti, K. (2022). 'PREDICTION OF THE WEAR RATES OF EPOXY-BASED NANOCOMPOSITES USING ARTIFICIAL NEURAL NETWORK APPROACH', EKB Journal Management System, 17(63), pp. 569-576. doi: 10.21608/auej.2022.233841
Al Shatti, K. PREDICTION OF THE WEAR RATES OF EPOXY-BASED NANOCOMPOSITES USING ARTIFICIAL NEURAL NETWORK APPROACH. EKB Journal Management System, 2022; 17(63): 569-576. doi: 10.21608/auej.2022.233841

PREDICTION OF THE WEAR RATES OF EPOXY-BASED NANOCOMPOSITES USING ARTIFICIAL NEURAL NETWORK APPROACH

Journal of Al-Azhar University Engineering Sector
Article 9, Volume 17, Issue 63, April 2022, Page 569-576  XML PDF (665.34 K)
Document Type: Original Article
DOI: 10.21608/auej.2022.233841
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Author
Khaled Al Shatti
Members of the Training Staff, the Public Authority for Applied Education and Training, Kuwait.
Abstract
The present investigation aims to use the artificial neural networks (ANN) approach to develop models that could be used to predict the wear rates of epoxy-based nanocomposites. The epoxy matrix was reinforced with different volume fractions (vol.-%) of boron carbide (BC) nanoparticles having average sizes of 50 nm. The BC nanoparticles were dispersed into the epoxy matrix using mechanical stirring technique. The wear tests were conducting under dry sliding conditions and several sliding speeds and applied loads. The developed ANN model was successfully used to predict the influence of the sliding speed, load, and BC nanoparticles volume fraction on wear rates of the epoxy/BC nanocomposites. The mean relative absolute error (MARE) of the developed ANN model was 4.27%.
 
يهدف البحث الحالي إلى استتخدام هه الشتبکات العيتبية انيتطهاعية ANN )لتطوير هماذج يمکن استتخدامها للتهب بمعدنت تکک للم تلفات الهاهومترية ذات األستتام من ماد اإليبوکستتيت تم تدعيم انيبوکستتي بحبيبات هاهومتريه حجم٪( من کربيد البورون BC ) بمتوستط حجم 50 هاهومتر باستتخدام تقهية التقليب الميکاهيکيت أجريت اختبارات التکک في ظروف اهزنق جاف وعد سترعات اهزنق وأحما مختلفةت تم استتتتخدام هموذج ANN المطور بهجاح للتهب بتأثير ستتترعة انهزنق والحم والهستتتبة الحجمية للحبيبات BC ) الهاهومترية على معدنت تکک للم تلفات الهاهومتريةت کان متوسط الخطأ اله سبي المطلق MARE )لهموذج ANN المطور 27.4٪ت
Keywords
Metal matrix Nanocomposites; Epoxy; Artificial Neural Network; Wear rate. الشبکات العصبية االصطناعية; االيبوکسى; المواد المؤتلفة النانومترية; معدل التآکل
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