Influence of optimization techniques and nano surface coating materials on microstructure of iron-nickel-chromium alloy using wire EDM process

R. Selvabharathi; R. Ganapathy Srinivasan; S. Palani

doi:10.2478/msp-2021-0003

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Influence of optimization techniques and nano surface coating materials on microstructure of iron-nickel-chromium alloy using wire EDM process

Materials Science-Poland

Volume 39 (2021): Issue 2 (June 2021)

By: R. Selvabharathi, R. Ganapathy Srinivasan and S. Palani

Open Access

|Aug 2021

Figures & Tables

Table 1

Chemical composition of alloy 20 (in wt%)

Si	S	P	Mn	C	Nb	Mo	Cu	Cr	Fe	Ni
1	0.035	0.045	2	0.07	1	3	4	21	29.8	38

Cause and effect diagram for performance characteristics in WEDM

Flow chart diagram of optimization techniques

Table 2

Process parameters employed for EDM

Parameter	Unit	Factor	Level
Parameter	Unit	Factor	1	2	3
Pulse on time	(μ sec)	A	106	116	126
Pulse off time	(μ sec)	B	40	50	60
Wire feed	(m/min)	C	4	8	12

Table 3

EDM process parameter of Taguchi L27 orthogonal array

Experimental trials	Factor
Experimental trials	A	B	C
1	1	1	1
2	1	1	2
3	1	1	3
4	1	2	1
5	1	2	2
6	1	2	3
7	1	3	1
8	1	3	2
9	1	3	3
10	2	1	1
11	2	1	2
12	2	1	3
13	2	2	1
14	2	2	2
15	2	2	3
16	2	3	1
17	2	3	2
18	2	3	3
19	3	1	1
20	3	1	2
21	3	1	3
22	3	2	1
23	3	2	2
24	3	2	3
25	3	3	1
26	3	3	2
27	3	3	3

Table 4

Cumulative experimental data of EDM process.

SI.NO	Pulse on time T ON (μ sec)	Pulse off time T OFF (μ sec)	Wire feed WF (m/min)	Material removal rate MRR (mm/min)	Surface roughness SR (μm)
1	106	40	4	0.68	1.385
2	106	40	8	0.71	1.255
3	106	40	12	0.505	1.175
4	106	50	8	0.48	1.35
5	106	50	12	0.405	1.265
6	106	50	4	0.2	1.105
7	106	60	12	0.315	1.35
8	106	60	4	0.165	1.14
9	106	60	8	0.165	1.08
10	116	40	12	2.405	2.57
11	116	40	4	2.205	2.39
12	116	40	8	0.75	1.435
13	116	50	4	1.54	2.525
14	116	50	8	0.58	1.38
15	116	50	12	0.66	1.72
16	116	60	8	0.47	1.63
17	116	60	12	0.625	1.85
18	116	60	4	0.5	1.625
19	126	40	8	3.4	2.9
20	126	40	12	1.26	1.85
21	126	40	4	2.2	2.66
22	126	50	12	0.91	2.08
23	126	50	4	1.66	2.635
24	126	50	8	1.54	2.445
25	126	60	4	1.34	2.855
26	126	60	8	1.345	2.56
27	126	60	12	0.29	1.295

BPNN model used for process parameter analysis and optimization techniques

Table 5

BPNN parameters and interlayer details

Name	Number	No.	MRR	SR
parameter. Show	50	1^st	9	9
Maximum parameter. Epochs	1000	1^st	9	9
Number of input parameter	3	2^nd	8	10
Number of output parameter	1	2^nd	8	10

Adaptive neuro-fuzzy interference system (ANFIS)

(a) Plot diagram of material removal rate; (b) Plot diagram of surface roughness SR (μm)

Table 6

Regression ratio of material removal rate

Source	DF	Adj SS	Adj MS	F-Value	P-Value
Regression	3	13.1260	4.3753	21.32	0.000
Pulse on time t on (μ sec)	1	7.8095	7.8095	38.06	0.450
Pulse off time t on (μ sec)	1	5.0545	5.0545	24.63	0.300
Wire feed	1	0.2620	0.2620	1.28	0.250
Error	23	4.7195	0.2052
Total	26	17.8454

Table 7

Regression ratio of surface roughness SR (μm)

Source	DF	Adj SS	Adj MS	F-Value	P-Value
Regression	3	1.96406	0.65469	16.14	0.000
Pulse on time t on (μ sec)	1	1.76964	1.76964	43.62	0.420
Pulse off time t on (μ sec)	1	0.08050	0.08050	1.98	0.470
Wire feed	1	0.11392	0.11392	2.81	0.210
Error	23	0.93314	0.04057
Total	26	2.89719

The best fitness value estimated by BPNN model of (a) Material removal rate (b) Surface roughness SR (μm)

Plot diagram of material removal rate of ANFIS model

Table 8

(a) 1^st EDM Process parameter of alloy 20 materials; (b) 2^nd EDM Process parameter of alloy 20 materials

No	MRR (mm/min)			SR (μm)
No	EOP	BPOP	ERROR	EOP	BPOP	ERROR
1	0.68	0.5643	11.57	1.385	1.3627	2.23
2	0.71	0.7451	−3.51	1.255	1.2712	−1.62
4	0.48	0.4921	−1.21	1.35	1.3215	2.85
10	2.405	2.4184	−1.34	2.57	2.3954	17.46
14	0.58	0.5683	1.17	1.38	1.4725	−9.25
17	0.625	1.0514	−42.64	1.85	2.3891	−53.91
22	0.91	0.9244	−1.44	2.08	2.0176	6.24
23	1.66	1.7587	−9.87	2.635	2.7871	−15.21
25	1.34	1.3572	−1.72	2.855	2.8142	4.08

No	MRR (mm/min)			SR (μm)
No	EOP	BPOP	ERROR	EOP	BPOP	ERROR
3	0.505	0.6383	−13.33	1.175	1.1254	4.96
6	0.2	0.1662	3.38	1.105	1.1031	0.19
9	0.165	0.1675	−0.25	1.08	1.1182	−3.82
12	0.75	0.7593	−0.93	1.435	1.4258	0.92
15	0.66	0.6247	3.53	1.72	1.7467	−2.67
18	0.5	0.7124	−21.24	1.625	1.6024	2.26
20	1.26	1.4972	−23.72	1.85	1.4358	41.42
24	1.54	1.5524	−1.24	2.445	2.5123	−6.73
26	1.345	1.3571	−1.21	2.56	2.5917	−3.17

Table 9

Results of BPNN-ANFIS optimization

Sl. No.	Input parameters			Output parameters
Sl. No.	Pulse on time (μ sec)	Pulse off time (μ sec)	Wire feed (m/min)	Optimal value (mm)	Machining performance
1.	110	80	10	0.184	Material removal rate
2.	106	60	10	1.95	Surface roughness

Plot diagram of surface roughness of ANFIS model

Table 10

EDM process parameters and error of confirmation experiment

	Input parameters			Output parameters
	Pulse on time (μ sec)	Pulse off time (μ sec)	Wire feed (m/min)	Material removal rate (mm/min)	Surface roughness (μm)
Experiment predicted by	106	60	8	0.155	1.08
BPNN-ANFIS model	105.7	59	8	0.1675	1.1182
Error %				−1.5	−3.5

SEM analysis a) Alloy 20 materials b) Coating materials c) Hybrid treated alloy 20 microstructure after machining

References

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Articles in this issue

DOI: https://doi.org/10.2478/msp-2021-0003 | Journal eISSN: 2083-134X (formerly 2083-124X) | Journal ISSN: 2083-1331

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Language: English

Page range: 152 - 165

Submitted on: Feb 10, 2021

Accepted on: Mar 14, 2021

Published on: Aug 9, 2021

Published by: Wroclaw University of Science and Technology

In partnership with: Paradigm Publishing Services

Keywords:

measurement optimization,

nano-coating,

iron-nickel-chromium,

microstructure,

wire EDM

Related subjects:

Materials sciences,

Materials sciences, other,

Nanomaterials,

Functional and smart materials,

Materials characterization and properties

© 2021 R. Selvabharathi, R. Ganapathy Srinivasan, S. Palani, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 39 (2021): Issue 2 (June 2021)

Influence of optimization techniques and nano surface coating materials on microstructure of iron-nickel-chromium alloy using wire EDM process

Figures & Tables

Table 1

Fig. 1

Fig. 2

Table 2

Table 3

Table 4

Fig. 3

Table 5

Fig. 4

Fig. 5

Table 6

Table 7

Fig. 6

Fig. 7

Table 8

Table 9

Fig. 8

Table 10

Fig. 9

Paradigm

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