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

Table 7

Training dataset for LM algorithm.

Exp. no.	Inputs			Target
	N (rev/min)	f (mm/rev)	2ρ (degree)	Fz (N)	Ra (μm)
1	1250	0.10	120	1250	0.576
2	1250	0.10	140	1300	0.585
3	1250	0.10	145	1375	0.592
4	1250	0.155	120	1265	0.582
5	1250	0.155	140	1360	0.592
6	1250	0.155	145	1420	0.61
10	1500	0.10	120	950	0.565
11	1500	0.10	140	1100	0.598
12	1500	0.10	145	1150	0.635
13	1500	0.155	120	990	0.586
14	1500	0.155	140	1110	0.642
15	1500	0.155	145	1200	0.685
19	1750	0.10	120	900	0.585
20	1750	0.10	140	970	0.612
21	1750	0.10	145	1000	0.651
22	1750	0.155	120	925	0.535
23	1750	0.155	140	1010	0.545
24	1750	0.155	145	1050	0.564