Optimization of Processing Parameters in ECM of Die Tool Steel Using Nanofluid by Multiobjective Genetic Algorithm

Table 3

Process decision variables along with optimized response from GA for aqueous NaNO₃.

Sl. number	Voltage (V)	Feed rate (mm/min)	Discharge rate (lit/min)	MRR (mm^3/min)	Surface roughness (micron)
1	12	0.1100281	8.134412	132.46309	1.513575
2	12.030211	0.1104329	9.087866	152.49089	1.777581
3	12.014045	0.1039534	10.04123	169.34532	2.152654
4	17.995146	0.5265484	11.98002	302.51291	2.656720
5	12.000245	0.1005786	9.147932	153.98288	1.791250
6	17.991590	0.3675932	11.97936	260.68191	2.254694
7	12.006746	0.2003393	8.234414	137.00311	1.560310
8	12.008757	0.1019808	10.06793	169.87964	2.159093
9	17.989152	0.3175749	11.99351	250.47074	2.189059
10	12.000737	0.1004096	9.719882	164.05383	2.038806
11	12.009474	0.2017167	8.488291	141.77756	1.587574
12	17.994398	0.5169848	11.97913	299.58104	2.623729
13	17.985153	0.2530979	11.99503	239.31519	2.164326
14	17.988464	0.3482414	11.99409	256.55458	2.219778
15	12.012065	0.1043002	8.710513	145.82497	1.564311
16	17.990082	0.4115904	11.98257	270.80421	2.330752
17	17.986068	0.3813624	11.99307	263.68107	2.268322
18	12.004311	0.1041213	8.848672	148.38304	1.639634
19	12.000057	0.2000266	8.109496	134.59464	1.771653
20	12.015943	0.1038107	8.938570	150.04776	1.691262
21	17.988003	0.4437773	11.99324	278.90557	2.397192
22	12.001201	0.1004778	8.995683	151.23304	1.714381
23	12	0.2	8.874121	132.46309	1.693575
24	17.995820	0.5399502	11.97976	306.69449	2.706127
25	12.007139	0.1076954	8.422892	140.26985	1.592297
26	17.985182	0.2532877	11.99497	239.34474	2.164350
27	12.013497	0.1039507	9.351800	157.47043	1.892639