A machine learning approach in drilling of E-glass woven composites

• Ravindranadh Bobbili ^[1] ; Vemuri Madhu ^[1]
  1. [1] Defence Metallurgical Research Laboratory

     Defence Metallurgical Research Laboratory

     India

     
• Localización: Mechanics based design of structures and machines, ISSN 1539-7734, Vol. 50, Nº. 3, 2022, págs. 1081-1089
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Drilling trials were conducted on E glass woven composites and machine learning models were employed for correlating drilling variables namely point angle, feed rate and spindle speed with response measures. The thrust force, surface roughness (Ra) and burr height were considered as performance characteristics in this study. Using self-organizing map (SOM) method, Neighboring weight distances in SOM with 10 10 neurons, Hit map corresponding to the neighboring weight distances, Wight planes showing weight distributions were developed. The validation tests have also been conducted to verify the results obtained by ANN technique. The predictions of the artificial neural network (ANN) model result were in good agreement with experimental results.