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63 ta ish

Ish: Probing the impact of process variables in laser-welded aluminum alloys: A machine learning study

  1. Bayesian machine learning-aided approach bridges between dynamic elasticity and compressive strength in the cement-based mortars

    Ning Wang, Majid Samavatian, Vahid Samavatian +1

    Maqola20238 iqtibos
    ABI

  2. Applications of machine learning in friction stir welding: Prediction of joint properties, real-time control and tool failure diagnosis

    Ammar H. Elsheikh

    Maqola20233 iqtibos
    ABI

  3. Discovery of novel quaternary bulk metallic glasses using a developed correlation-based neural network approach

    Majid Samavatian, Reza Gholamipour, Vahid Samavatian

    Maqola20202 iqtibos
    ABI

  4. Friction Stir Welding of Aluminum in the Aerospace Industry: The Current Progress and State-of-the-Art Review

    Mohamed M. Z. Ahmed, Mohamed M. El-Sayed Seleman, Dariusz Fydrych +1

    Sharh maqolaOchiq kirish20232 iqtibos
    ABI

  5. A review on lightweight materials for defence applications: Present and future developments

    Suchart Siengchin

    Sharh maqolaOchiq kirish20232 iqtibos
    ABI

  6. Hybrid particle swarm optimization with extreme learning machine for daily reference evapotranspiration prediction from limited climatic data

    Bin Zhu, Yu Feng, Daozhi Gong +3

    Maqola20202 iqtibos
    ABI

  7. Improving the surface quality of friction stir welds using reinforcement learning and Bayesian optimization

    Roman Hartl, Julius Hansjakob, Michael F. Zaeh

    MaqolaOchiq kirish20202 iqtibos
    ABI

  8. Bayesian approach for inferrable machine learning models of process–structure–property linkages in complex concentrated alloys

    George Stephen Thoppil, Jian‐Feng Nie, Alankar Alankar

    Maqola20232 iqtibos
    ABI

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