To address the limited prediction performance of PR under complex scenarios, more advanced neural network models, such as BPNN, have been increasingly employed. Soepangkat et al. utilized BPNN to predict the drilling thrust force, torque and delamination damage factor with different tool geometry, feed rate and spindle speed. The experimental and predicted hole entry/exit delamination is shown in Fig. (a) and the BPNN achieved a prediction error of <10%. Ahmad et al. predicted the cutting force coefficient (K t and K r ) at different fibre cutting orientation, tool rotation angle, feed rate and depth of cut using BPNN. As shown in Fig. (b), this model can successfully predict the continuous changing of cutting force (F x , F y ) in slot milling of CFRP. Wang et al. deployed BPNN to predict the thrust force and delamination factor with different drill bit point angle, feed rate and spindle speed. The average relative error is 3.04% and 1.36%, respectively for the thrust force and delamination factor. Despite its effectiveness, it is worth noting BPNN has its own limitations: