Random Forest Modeling for Prediction of Thrust Force During Drilling of Flax/PP Composite

The current investigation presents a random forest modeling approach for the prediction of thrust force during the drilling of flax/polypropylene (PP) composites. The drilling of composites is a complex process due to the generation of high thrust force and heterogeneous nature of the material. Therefore, accurate prediction of the thrust force is crucial for improving the quality of the drilled holes. In this investigation, various input parameters, such as, spindle speed, feed rate, and drill geometry are considered, and the random forest model is trained to predict the thrust force. The results show that the random forest model provides accurate predictions of the thrust force during the drilling of flax/PP composites. The model's performance is evaluated using various metrics, such as, mean absolute error, mean squared error, and mean absolute percentage error. The results show that the feed rate is the most significant input parameter, followed by drill geometry and spindle speed. The study demonstrates the effectiveness of the random forest modeling approach for the prediction of thrust force during the drilling of composite materials and provides in-sights into the influence of input parameters on the drilling process. The findings of this study can be useful for researchers working in the broad area of drilling of composite materials and can aid in the optimization of drilling parameters for improved performance and quality of composite products with drilled holes.