The Flexural Strength Prediction of Porous Cu-Sn-Ti Composites via Artificial Neural Networks

• Main Authors: El Sawy, Abdelrahman, Anwar, P. P. Abdul Majeed, Musa, Rabiu Muazu, Mohd Azraai, M. Razman, Mohd Hasnun Ariff, Hassan, Abdul Aziz, Jaafar
• Format: Book Section
• Language: English; English; English
• Published: Universiti Malaysia Pahang 2018
• Subjects: TS Manufactures
• Online Access: http://umpir.ump.edu.my/id/eprint/24528/1/62.%20The%20flexural%20strength%20prediction%20of%20porous%20cu-sn-ti.pdf; http://umpir.ump.edu.my/id/eprint/24528/8/8.%20The%20flexural%20strength%20prediction%20of%20porous%20Cu-Sn-Ti%20composites%20via%20artificial%20neural%20networks.pdf; http://umpir.ump.edu.my/id/eprint/24528/9/8.1%20The%20flexural%20strength%20prediction%20of%20porous%20Cu-Sn-Ti%20composites%20via%20artificial%20neural%20networks.pdf; http://umpir.ump.edu.my/id/eprint/24528/; https://link.springer.com/chapter/10.1007/978-981-13-8323-6_34; https://doi.org/10.1007/978-981-13-8323-6_34
• Institution: Universiti Malaysia Pahang

Porous alloy-composites have demonstrated excellent qualities with regards to grinding superalloys. Flexural strength is an important mechanical property associated with the porosity level as well as inhomogeneity in porous composites. Owing to the non-linear characteristics of the constituents of t...