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ABSTRACT A hybrid machine learning (ML)-assisted framework was developed to predict and optimize abrasive waterjet (AWJ) machining of multi-walled carbon nanotube (MWCNT)-infused magnesium fibre metal laminates (Mg-FMLs). Thirty experiments were conducted using a four-factor Box–Behnken design, and bootstrap sampling-based augmentation generated 540 additional synthetic observations.
ABSTRACT Abrasive waterjet (AWJ) machining of magnesium-based fiber metal laminates (Mg-FMLs) reinforced with multi-walled carbon nanotubes (MWCNT) presents significant challenges due to the heterogeneous interaction between metallic skins and nano-modified composite layers.
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