Optimization of Drilling Parameters for Surface Quality of AISI 1045 Steel using ANN

Drilling machines play a crucial role in manufacturing, where optimizing process parameters is essential to achieving high-quality surface finishes. Improper parameter selection can lead to poor machining performance, increased tool wear, and suboptimal surface integrity. This study investigates the effects of cutting speed, feed rate, and cutting environment on the surface quality of AISI 1045 steel using a high-speed steel (HSS) drill bit. A systematic Taguchi experimental design was employed to efficiently structure the trials, while signal-to-noise (S/N) ratio and analysis of variance (ANOVA) were utilized to determine the most influential parameters. To further enhance predictive accuracy, an Artificial Neural Network (ANN) model was developed, which demonstrated a high correlation between experimental and predicted values. The findings underscore the significant influence of machining parameters on surface roughness and highlight ANN’s superior capability in optimizing drilling conditions for improved manufacturing precision and efficiency.