A Machine Learning Approach to Optimize, Model, and Predict the Machining Factors in Dry Drilling of Nimonic C263

Table 8

FFNN model results for test dataset using the LM algorithm.

Exp. no.	Inputs			Predicted output
	N (rev/min)	f (mm/rev)	2ρ (degree)	Fz (N)	Ra (μm)
7	1250	0.198	120	787.1942	0.600289
8	1250	0.198	140	1086.899	0.681932
9	1250	0.198	145	1153.774	0.651407
16	1500	0.198	120	737.7439	0.597503
17	1500	0.198	140	1090.856	0.649399
18	1500	0.198	145	1106.866	0.681351
25	1750	0.198	120	738.4216	0.596766
26	1750	0.198	140	1135.186	0.555578
27	1750	0.198	145	1159.582	0.582648