Research Article

Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network

Table 3

Listing of four-level full factorial samples.

Sample IDGroupCutting speed (m/min)Depth of cut (mm)Feed (mm/rev)Surface Ra (µm)MRR (mm3/min)

111250.300.050.1851875
21250.300.100.4573750
31250.300.150.9725625
41250.300.201.7797500
521250.600.050.1883750
61250.600.100.3757500
71250.600.150.99811250
81250.600.202.20515000
931250.900.050.1685625
101250.900.100.37811250
111250.900.151.05916875
121250.900.202.25422500
1341251.120.050.1447000
141251.120.100.46614000
151251.120.151.15421000
161251.120.202.78628000
1751500.300.050.1562250
181500.300.100.3654500
191500.300.150.9546750
201500.300.201.7379000
2161500.600.050.1434500
221500.600.100.3499000
231500.600.151.105813500
241500.600.201.86618000
2571500.900.050.1286750
261500.900.100.41313500
271500.900.150.99920250
281500.900.201.78227000
2981501.120.050.2018400
301501.120.100.50816800
311501.120.151.02025200
321501.120.201.91133600
3391750.300.050.1992625
341750.300.100.3865250
351750.300.150.9827875
361750.300.202.00510500
37101750.600.050.1995250
381750.600.100.43210500
391750.600.151.12815750
401750.600.202.05421000
41111750.900.050.2567875
421750.900.100.48615750
431750.900.151.18723625
441750.900.202.75931500
45121751.120.050.1789800
461751.120.100.46419600
471751.120.151.31629400
481751.120.202.21439200
49132000.300.050.1453000
502000.300.100.3986000
512000.300.150.9559000
522000.300.202.21112000
53142000.600.050.2656000
542000.600.100.48712000
552000.600.150.99918000
562000.600.201.99924000
57152000.900.050.1489000
582000.900.100.47718000
592000.900.150.98727000
602000.900.202.16536000
61162001.120.050.25411200
622001.120.100.34522400
632001.120.151.11533600
642001.120.201.89644800