Python inspired artificial neural networks modeling in drilling of glass-hemp-flax fiber composites

Abstract

As composites are materials whose properties can essentially be customized to suit the necessities of the engineering application on hand, they are being widely used in many applications for radically different purposes. In order to ensure quality in production process of composite products, a solid understanding of the process involved during its manufacturing is essential to ensure the product is free from both internal and external defects. To that aim, a study was conducted to model Thrust force and Torque on drilling of Glass-Hemp-Flax reinforced polymer composite by fabricating and maching the composite as per Taguchi's L 27 Orthogonal Array. The process parameters considered for modeling are drill diameter, spindle speed and feed rate. Using the process control parameters as inputs and thrust force and torque to be predicted as outputs, artificial neural networks (ANNs) were created to model the effects of the inputs and their interactions. The predictions obtained from the neural networks were compared with the values obtained from experimentation. Excellent agreement was found between the two sets of values, establishing grounds for more extensive use of neural networks in modelling of machining parameters.