المؤلفون: Zhang, Le, El-Shabrawi, Mortada, Baur, Louise A., Byrne, Christopher D., Targher, Giovanni, Kehar, Mohit, Porta, Gilda, Lee, Way Seah, Lefere, Sander, Turan, Serap, Alisi, Anna, Weiss, Ram, Faienza, Maria Felicia, Ashraf, Ambika, Sundaram, Shikha S., Srivastava, Anshu, De Bruyne, Ruth, Kang, Yunkoo, Bacopoulou, Flora, Zhou, Yong-Hai, Darma, Andy, Lupsor-Platon, Monica, Hamaguchi, Masahide, Misra, Anoop, Méndez-Sánchez, Nahum, Ng, Nicholas Beng Hui, Marcus, Claude, Staiano, Amanda E., Waheed, Nadia, Alqahtani, Saleh A., Giannini, Cosimo, Ocama, Ponsiano, Nguyen, Mindie H., Arias-Loste, Maria Teresa, Ahmed, Mohamed Rabea, Sebastiani, Giada, Poovorawan, Yong, Al Mahtab, Mamun, Pericàs, Juan M., Reverbel da Silveira, Themis, Hegyi, Peter, Azaz, Amer, Isa, Hasan M., Lertudomphonwanit, Chatmanee, Farrag, Mona Issa, Nugud, Ahmed Abd Alwahab, Du, Hong-Wei, Qi, Ke-Min, Mouane, Nezha, Cheng, Xin-Ran, Al Lawati, Tawfiq, Fagundes, Eleonora D.T., Ghazinyan, Hasmik, Hadjipanayis, Adamos, Fan, Jian-Gao, Gimiga, Nicoleta, Kamal, Naglaa M., Ștefănescu, Gabriela, Hong, Li, Diaconescu, Smaranda, Li, Ming, George, Jacob, Zheng, Ming-Hua

المصدر: MED ; ISSN: 2666-6359 ; ISSN: 2666-6340

مصطلحات موضوعية: Medicine and Health Sciences

وصف الملف: application/pdf

العلاقة: https://biblio.ugent.be/publication/01HWX4Y27Z2T49FTA4ZD0800N9Test; http://hdl.handle.net/1854/LU-01HWX4Y27Z2T49FTA4ZD0800N9Test; http://doi.org/10.1016/j.medj.2024.03.017Test; https://biblio.ugent.be/publication/01HWX4Y27Z2T49FTA4ZD0800N9/file/01HWXB4GP8VBC519WTXG75GEMJTest

الإتاحة: https://doi.org/10.1016/j.medj.2024.03.017Test
https://biblio.ugent.be/publication/01HWX4Y27Z2T49FTA4ZD0800N9Test
http://hdl.handle.net/1854/LU-01HWX4Y27Z2T49FTA4ZD0800N9Test
https://biblio.ugent.be/publication/01HWX4Y27Z2T49FTA4ZD0800N9/file/01HWXB4GP8VBC519WTXG75GEMJTest

المؤلفون: Gao, Xiu-hua, Qi, Ke-min, Deng, Tian-yong, Qiu, Chun-lin, Zhou, Ping, Du, Xian-bin

المصدر: Journal of Iron and Steel Research International ; volume 13, issue 6, page 71-73 ; ISSN 1006-706X 2210-3988

مصطلحات موضوعية: Materials Chemistry, Metals and Alloys, Mechanics of Materials

الإتاحة: https://doi.org/10.1016/s1006-706xTest(06)60114-3
http://api.elsevier.com/content/article/PII:S1006706X06601143?httpAccept=text/xmlTest
http://api.elsevier.com/content/article/PII:S1006706X06601143?httpAccept=text/plainTest

المؤلفون: Gao, Xiu Hua, Deng, Tian Yong, Wang, Hao Ran, Qiu, Chun Lin, Qi, Ke Min, Zhou, Ping

المصدر: Advanced Materials Research; June 2010, Vol. 118 Issue: 1 p332-335, 4p

مستخلص: The prediction of the hardenability of gear steel has been carried using stepwise polynomial regression and artificial neural networks (ANN). The software was programmed to quantitatively predict the hardenability of gear steel by its chemical composition using two calculating models respectively. The prediction results using artificial neural networks have more precise than the stepwise polynomial regression model. The predicted values of the ANN coincide well with the actual data. So an important foundation has been laid for prediction and controlling the production of gear steel.