Работы, цитирующие эту работу

Работ: 5

Работа: Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics

1. A micromechanical nested machine learning model for characterizing materials behaviors of bulk metallic glasses

   Moustafa Sahnoune Chaouche, Hani K. Al-Mohair, Shavan Askar +3

   СтатьяMetallic Glasses and Amorphous AlloysJournal of Non-Crystalline Solids2023Цитирований: 1

   ABIABI:AkademIndex/openalex/2023.article.001191

2. Tribological Properties Assessment of Metallic Glasses Through a Genetic Algorithm-Optimized Machine Learning Model

   Untung Rahardja, Arif Sarı, Ali Alsalamy +3

   СтатьяMetallic Glasses and Amorphous AlloysMetals and Materials International2023Цитирований: 0

   ABIABI:AkademIndex/openalex/2023.article.000099

3. An embedded machine learning strategy for analyzing interfacial characteristics in impact welding of dissimilar alloys

   Arif Sarı, Candra Zonyfar, Shavan Askar +3

   СтатьяWelding Techniques and Residual StressesComposite Interfaces2023Цитирований: 0

   ABIABI:AkademIndex/openalex/2023.article.001421

4. Estimation of compressive strength of cement mortars using impulse excitation technique and a genetic algorithm

   Muftah Mohamed Baroud, Arif Sarı, Sherzod Abdullaev +2

   СтатьяInnovative concrete reinforcement materialsAdvances in Cement Research2023Цитирований: 0

   ABIABI:AkademIndex/openalex/2023.article.003920

5. Machine Learning-Based Analysis of Surface Hardening in Shot-Peened Superalloys

   Paul Rodrigues, Mokhtar Massoud Kerwad, Shavan Askar +3

   СтатьяSurface Treatment and Residual StressJournal of Materials Engineering and Performance2024Цитирований: 0

   ABIABI:AkademIndex/openalex/2024.article.002734