Predicting drilling parameters for producing quality holes through optimally configured gated recurrent unit

Predicting drilling parameters accurately to guarantee the creation of high-quality holes is a critical task in the field of precision machining. The optimisation of drilling parameters for excellent hole quality is the main subject of this scientific study paper, which explores the field of predictive modelling. In order to predict thrust force, delamination, and surface roughness, the research presents a unique method that utilizes the Gated Recurrent Unit (GRU) and recurrent neural network (RNN) architecture. The main goal is to use an Adaptive Fire Hawk Optimiser (AFHO) to guide the GRU model towards ideal hyperparameters. Through systematic experimentation and meticulous analysis, the study showcases the model's capability to forecast drilling outcomes with unprecedented precision, thereby streamlining the machining process and contributing to the advancement of quality hole production.