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Optimizing Machining Processes for Hybrid Glass Fiber-Reinforced Polymeric Nanocomposites: A Radial Basis Function Approach
Summary
Researchers applied an artificial intelligence-based Radial Basis Function (RBF) model to optimize drilling processes for hybrid glass fiber-reinforced polymeric nanocomposites (HGFRP), improving machining performance across multiple process parameters. The study demonstrates that AI-based optimization can meaningfully reduce defects and improve quality in advanced composite manufacturing.
Hybrid glass fiber-reinforced polymeric nanocomposites (HGFRP) play a crucial role in various industries.However, optimizing drilling processes for these materials is essential.This research addresses this need by employing an artificial intelligence-based Radial Basis Function (RBF) model to efficiently optimize machining parameters, ensuring improved performance and cost-effectiveness across diverse industrial applications.The outcomes derived from the RBF model showcase an impressive Mean Squared Error (MSE) of 0.526.This superior performance surpasses that of both the Artificial Neural Network (ANN) and the Random Forest (RF) models in predicting response parameters such as delamination, thrust force, and torque within specified machining parameters, including spindle speed, feed rate, and drill diameter.The RBF attains an average MSE of 0.0021, 0.4145, and 0.13497 for the Delamination factor, Thrust force, and Torque, respectively, which outperforms comparative techniques.These outcomes contribute to the enhancement of manufacturing processes, elevate product quality, and ultimately bolster industrial competitiveness across various sectors.The knowledge and guidelines derived from this research pave the way for more efficient and precise machining of HGFRP, delivering tangible benefits to diverse industries.
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