Research Article

Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network

Table 4


Sample IDGroupSpindle speed (rpm)Depth of cut (mm)Table feed (mm/min)Surface Ra (μm)Material removal rate (MRR) mm3/min

115000.5500.1851000
25000.51000.2262000
35000.51500.3323000
45000.52000.4034000

525001500.212000
650011000.2254000
750011500.3276000
850012000.378000

935001.5500.1773000
105001.51000.2176000
115001.51500.2599000
125001.52000.31312000

1345002500.1644000
1450021000.1748000
1550021500.2212000
1650022000.24816000

17510000.5500.1331000
1810000.51000.1842000
1910000.51500.1973000
2010000.52000.2454000

21610001500.142000
22100011000.1894000
23100011500.2076000
24100012000.2288000

25710001.5500.1543000
2610001.51000.196000
2710001.51500.2029000
2810001.52000.25812000

29810002500.1354000
30100021000.28000
31100021500.23812000
32100022000.27416000

33915000.5500.1151000
3415000.51000.1852000
3515000.51500.2153000
3615000.52000.2424000

371015001500.1562000
38150011000.1814000
39150011500.1856000
40150012000.2248000

411115001.5500.1373000
4215001.51000.1496000
4315001.51500.2179000
4415001.52000.2412000

451215002500.124000
46150021000.1498000
47150021500.19112000
48150022000.23516000

491320000.5500.1361000
5020000.51000.1532000
5120000.51500.1873000
5220000.52000.2284000

531420001500.1532000
54200011000.1634000
55200011500.2136000
56200012000.228000

571520001.5500.143000
5820001.51000.1556000
5920001.51500.2179000
6020001.52000.22812000

611620002500.184000
62200021000.2258000
63200021500.25512000
64200022000.26816000