Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

Table 1

Surface roughness measurements for experiment (reproduced with permission from Bolat [23]).

Trial number	Feed rate (μm/rev)	Depth of cut (μm)	Spindle speed (rpm)	VX (mgRMS)	VY (mgRMS)	VZ (mgRMS)	Ra (nm)	Type
1	5	40	2000	28.07	45.97	41.69	17.90	Training dataset
2	9	22.5	1000	27.90	52.76	37.75	15.0
3	5	22.5	2000	27.86	44.58	38.87	16.50
4	9	22.5	2000	29.90	61.70	46.74	33.70
5	9	5	1500	26.97	43.16	37.54	19.70
6	5	5	2000	25.35	49.91	35.04	9.40
7	1	40	1000	28.74	44.8	39.13	10.10
8	1	5	2000	25.17	45.21	32.67	2.70
9	1	40	2000	26.54	56.23	35.81	9.50
10	5	5	1500	27.09	52.08	36.90	12.0
11	1	22.5	2000	26.45	52.67	36.93	6.40
12	1	40	1500	28.75	52.60	35.59	9.20
13	5	40	1000	32.77	66.69	38.66	12.80
14	9	5	1000	26.06	54.75	35.74	11.50
15	1	22.5	1000	25.88	48.86	33.23	8.30
16	1	5	1000	28.14	54.58	37.88	4.70
17	5	40	1500	26.01	50.85	40.10	13.60
18	9	40	1000	25.64	48.62	33.21	25.90
19	1	22.5	1500	25.25	44.57	33.09	4.30
20	9	5	2000	24.65	50.64	34.67	22.10
21	2	3	1250	6.90	28.61	59.82	47.17
22	7	22	1000	18.10	28.63	59.83	46.42
23	2	6	2250	6.30	28.76	60.88	49.09
24	7	30	1750	23.0	37.26	78.91	54.13
25	12	40	1250	52.10	28.59	59.90	46.48
26	2	50	1000	5.80	28.92	60.0	46.70
27	7	20	2250	14.40	28.91	59.77	46.93
28	5	22.5	1000	26.72	52.41	37.35	12.20	Testing dataset
29	1	5	1500	25.53	46.66	33.04	3.70
30	5	5	1000	28.91	58.81	46.34	10.20
31	5	22.5	1500	25.15	46.91	33.72	8.50
32	9	40	2000	36.22	74.97	45.09	46.80
33	9	40	1500	25.61	48.27	33.20	21.60
34	9	22.5	1500	28.301	59.02	46.32	26.40
35	12	10	1750	47.70	28.16	58.97	47.48