Experimental Investigations on µED Milling of Inconel 718 with Nano SiC Abrasive Mixed Dielectric

Elumalai, B.; Gowri, S.; Hariharan, P.; Pillai, K.V. Arun

doi:10.1590/1980-5373-MR-2021-0468

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Article • Mat. Res. 25 • 2022 • https://doi.org/10.1590/1980-5373-MR-2021-0468 copy

Experimental Investigations on µED Milling of Inconel 718 with Nano SiC Abrasive Mixed Dielectric

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

Micro electro-discharge machining is one of the efficient processes to create three-dimensional micro features of metallic components for various applications. Powder mixed EDM improves the machining rate and reduces the surface roughness by evenly distributing the spark. The present studydemonstrates the effect of SiC nanopowder on the machining of Inconel 718 at different Discharge Energy Regimes (DER). Significant improvement in MRR, reduction in TWR and surface roughness were observed in nanopowder mixed micro-EDM (NPMμEDM) compared with micro-EDM. The nano additive considerably improved the Material Removal Rate (MRR) by163% and reduced the Tool Wear Rate (TWR)and surface roughness by 24%, 17% respectively. Models were created to predict the Surface Roughness in NPMμEDM using two different approaches namely Support Vector Regression (SVR) and Random Forest Machine (RFM). Both SVR and RFM models were able to predict the R_a value with better accuracies.

Keywords:
Micro-EDM; Silicon carbide nanoparticle; Inconel 718; Machine learning

Parameter	Unit	Levels
Discharge Energy, $E = \frac{1}{2} C V^{2}$	μJ	Low	3.2, 4.05, 5, 6.05, 7.2
		Medium	32, 40.5, 50, 60.5, 72
		High	320, 405, 500, 605, 720

Sl. No.	Capacitance (μF)	Voltage (V)	Energy (μJ)	Powder (g/L)	Ra (μm)
					Actual	Predicted
					Actual	SVR (Polynomial)	SVR (RBF)	Random Forest
1	0.001	80	3.2	0	0.303	0.413	0.433	0.435
2	0.001	90	4.05	0	0.293	0.298	0.294	0.338
3	0.001	100	5	0	0.364	0.362	0.366	0.396
4	0.001	110	6.05	0	0.317	0.314	0.319	0.369
5	0.001	120	7.2	0	0.337	0.340	0.336	0.357
6	0.01	80	32	0	0.848	0.713	0.692	0.732
7	0.01	90	40.5	0	0.836	0.839	0.837	0.841
8	0.01	100	50	0	0.862	0.860	0.863	0.843
9	0.01	110	60.5	0	0.903	0.907	0.905	0.841
10	0.01	120	72	0	0.924	0.921	0.926	0.875
11	0.1	80	320	0	1.05	1.054	1.049	0.969
12	0.1	90	405	0	1.11	1.107	1.110	1.038
13	0.1	100	500	0	1.14	1.141	1.139	1.110
14	0.1	110	605	0	1.16	1.029	0.976	0.981
15	0.1	120	720	0	1.18	1.177	1.180	1.108
16	0.001	80	3.2	0.2	0.253	0.259	0.254	0.361
17	0.001	90	4.05	0.2	0.274	0.276	0.279	0.309
18	0.001	100	5	0.2	0.256	0.265	0.259	0.337
19	0.001	110	6.05	0.2	0.237	0.241	0.241	0.303
20	0.001	120	7.2	0.2	0.242	0.255	0.245	0.311
21	0.01	80	32	0.2	0.657	0.669	0.660	0.655
22	0.01	90	40.5	0.2	0.721	0.736	0.722	0.775
23	0.01	100	50	0.2	0.748	0.758	0.750	0.757
24	0.01	110	60.5	0.2	0.778	0.784	0.775	0.759
25	0.01	120	72	0.2	0.824	0.816	0.824	0.799
26	0.1	80	320	0.2	1.02	1.012	1.016	0.941
27	0.1	90	405	0.2	1.17	1.157	1.166	1.005
28	0.1	100	500	0.2	1.43	1.415	1.350	1.112
29	0.1	110	605	0.2	1.02	1.019	1.015	0.977
30	0.1	120	720	0.2	1.09	1.087	1.087	1.101
31	0.001	80	3.2	0.3	0.32	0.315	0.321	0.364
32	0.001	90	4.05	0.3	0.325	0.319	0.329	0.310
33	0.001	100	5	0.3	0.351	0.337	0.349	0.349
34	0.001	110	6.05	0.3	0.348	0.332	0.346	0.313
35	0.001	120	7.2	0.3	0.296	0.302	0.297	0.325
36	0.01	80	32	0.3	0.575	0.591	0.578	0.622
37	0.01	90	40.5	0.3	0.883	0.697	0.704	0.716
38	0.01	100	50	0.3	0.688	0.699	0.691	0.717
39	0.01	110	60.5	0.3	0.666	0.668	0.669	0.708
40	0.01	120	72	0.3	0.697	0.704	0.702	0.733
41	0.1	80	320	0.3	0.817	0.822	0.820	0.861
42	0.1	90	405	0.3	0.845	0.849	0.846	0.940
43	0.1	100	500	0.3	0.897	0.898	0.900	1.018
44	0.1	110	605	0.3	0.994	0.986	0.990	0.930
45	0.1	120	720	0.3	1.18	1.165	1.175	1.091
46	0.001	80	3.2	0.4	0.292	0.308	0.381	0.383
47	0.001	90	4.05	0.4	0.342	0.336	0.340	0.326
48	0.001	100	5	0.4	0.338	0.323	0.342	0.359
49	0.001	110	6.05	0.4	0.316	0.305	0.320	0.316
50	0.001	120	7.2	0.4	0.381	0.374	0.375	0.333
51	0.01	80	32	0.4	0.623	0.613	0.620	0.636
52	0.01	90	40.5	0.4	0.713	0.707	0.707	0.728
53	0.01	100	50	0.4	0.697	0.704	0.698	0.728
54	0.01	110	60.5	0.4	0.702	0.715	0.706	0.724
55	0.01	120	72	0.4	0.724	0.740	0.751	0.755
56	0.1	80	320	0.4	0.828	0.838	0.830	0.875
57	0.1	90	405	0.4	0.887	0.894	0.887	0.945
58	0.1	100	500	0.4	0.848	0.852	0.848	1.024
59	0.1	110	605	0.4	0.947	0.944	0.943	0.937
60	0.1	120	720	0.4	0.989	1.000	0.990	1.076

Model	R²	RMSE
Polynomial-SVR	0.979	0.045
RBF-SVR	0.986	0.038
Random Forest machine	0.934	0.081

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[1] *e-mail: elumalainathan@gmail.com