Evaluation of machinability performance of T51603 using response surface methodology and grey relational analysis

Kumar, R. Suresh; Vinodh, S.; Satishkumar, P.; Seenivasan, S.

doi:10.1590/1517-7076-RMAT-2023-0322

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Articles • Matéria (Rio J.) 29 (1) • 2024 • https://doi.org/10.1590/1517-7076-RMAT-2023-0322 copy

Evaluation of machinability performance of T51603 using response surface methodology and grey relational analysis

Authorship SCIMAGO INSTITUTIONS RANKINGS

The goal of this study is to increase material removal rate (M_rr), and minimize consumption of power (P_c) and surface integrity (S_r) while using the least amount of resources thereby addressing sustainable manufacturing and optimization in machining operation. Box Behnken Design (BBD) and Grey Regression Analysis (GRA) are systematically followed in the machining process on UNS T51603. The experimental runs were performed based on BBD followed by multi-objective optimization using GRA. The practical applicability and reliability of the optimized parameters is evaluated by confirmatory runs, and the optimal solution of single and multi-objective solution for S_r, M_rr, and P_c, is verified. The lowest S_r was achieved when S_s was maintained at 2000 rpm, with D_c at 0.6 mm, F_r at 750 mm/min, and C_fr 6 l/min. maximum M_rr was attained when S_s assigned at 1750 rpm, with D_c at 0.6 mm, F_r at 750 mm/min, and C_fr 8 l/min. When compared to confirmatory runs, the optimized set of parameters for BBD and GRA reveals a 10% variance, demonstrating the validity of the optimization strategies used. In terms of P_c the optimized parameters were found to be 1750 rpm, 0.2 mm, 500 mm/min, and 6 l/min.

Keywords:
CNC end milling; Grey regression analysis; Box Behnken; Surface roughness; Material removal rate

Ni	Si	C	Mn	S	P	Cr
1.2	0.39	0.10	0.58	0.02	0.03	0.48

PARAMETERS	LEVELS
PARAMETERS	1	2	3
S_s(rpm)	1500	1750	2000
D_c(mm)	0.2	0.4	0.6
F_r(mm/min)	500	750	1000
C_fr(l/min)	4	6	8

RUN ORDER	S_s (rpm)	D_c (mm)	F_r (mm/min)	C_fr (l/min)	S_r (µm)	M_rr (IPM)	P_c (HP)
1	1750	0.6	500	6	2.887	1.2111	1.131
2	1750	0.2	750	4	4.636	0.1944	0.251
3	1750	0.4	750	6	3.424	0.7636	0.806
4	2000	0.2	750	6	2.844	0.2171	0.262
5	1500	0.6	750	6	4.004	1.3101	1.349
6	1750	0.4	750	6	3.424	0.7636	0.806
7	1500	0.4	1000	6	4.54	0.8626	1.024
8	2000	0.4	750	8	1.632	0.7863	0.817
9	1500	0.2	750	6	5.324	0.3341	0.423
10	1750	0.4	750	6	3.424	0.7636	0.806
11	1750	0.4	500	8	2.995	0.8043	0.76
12	1750	0.4	500	4	4.099	0.6419	0.576
13	1750	0.6	1000	6	2.64	1.2921	1.406
14	1750	0.4	750	6	3.424	0.7636	0.806
15	1750	0.6	750	4	3.316	1.1704	1.177
16	2000	0.6	750	6	1.524	1.1931	1.188
17	1500	0.4	750	4	5.216	0.7409	0.794
18	2000	0.4	750	4	2.736	0.6239	0.633
19	1750	0.2	750	8	3.532	0.3568	0.435
20	1750	0.2	500	6	4.207	0.2351	0.205
21	2000	0.4	500	6	2.307	0.6646	0.588
22	1750	0.2	1000	6	3.96	0.3161	0.48
23	1750	0.6	750	8	2.212	1.3328	1.361
24	1750	0.4	1000	4	3.852	0.7229	0.851
25	2000	0.4	1000	6	2.06	0.7456	0.863
26	1500	0.4	750	8	4.112	0.9033	0.979
27	1750	0.4	750	6	3.424	0.7636	0.806
28	1750	0.4	1000	8	2.748	0.8853	1.035
29	1500	0.4	500	6	4.787	0.7816	0.749

ANOVA- S_r
SOURCE	SS	DF	MS	F-VALUE	P-VALUE
Model	26.83787	14	1.78556	627600.00	<0.0001	significant
S_s	18.6512	1	18.8512	627620.00	<0.0001
D_c	5.3272	1	5.2272	67590	<0.0001
F_r	0.193027	1	0.183027	5788618.94	<0.0001
C_fr	3.676448	1	3.656448	1866573.15	<0.0001
Residual Error	1.58533	14	0
Lack of Fit	0	10	0
Pure Error	0	4	0
Cor Total	27.8178	28

D_c	C_fr	S_s	F_r	S_r
0.6	6	2000	750	1.524

ANOVA – M_rr
SOURCE	SS	df	MS	F-VALUE	P-VALUE
Model	2.996899	4	0.78969	678091.19	<0.0001	significant
S_s	0.051167	1	0.06126	123432.95	<0.0001
D_c	2.887748	1	2.88762	769879.27	<0.0001
F_r	0.029686	1	0.02968	434180.42	<0.0001
C_fr	0.079521	1	0.08962	65660000	<0.0001
Residual Error	1.88653	24	0
Lack of Fit	0	20	0
Pure Error	0	4	0
Cor Total	2.997699	28

S_s	C_fr	D_c	F_r	M_rr
1750	8	0.6	750	1.3328

ANOVA - P_c
SOURCE	SS	df	MS	F-VALUE	P-VALUE
Model	2.97976	4	0.756819	565600.00	<0.0001	significant
A	0.078944	1	0.078957	514466.00	<0.0001
B	2.576728	1	2.575628	637800.00	<0.0001
C	0.236876	1	0.236877	507666.00	<0.0001
D	0.10211008	1	0.102130	46790.00	<0.0001
Residual Error	3.84833	24	0
Lack of Fit	0	20	0
Pure Error	0	4	0
Cor Total	2.9798794	28

S_s	D_c	F_r	C_fr	P_c
1750	0.2	500	6	0.205

NORMALIZED VALUES			DEVIATION SEQUENCE			GREY RELATION COEFFICIENTS			GREY RELATIONAL GRADE	RANK
S_r	P_C	M_rr	S_r	P_C	M_rr	S_r	P_C	M_rr	GREY RELATIONAL GRADE	RANK
0.641	0.107	0.177	0.359	0.893	0.823	0.582	0.359	0.378	0.440	24
0.181	1.000	0.950	0.819	0.000	0.050	0.379	1.000	0.910	0.763	3
0.500	0.500	0.463	0.500	0.500	0.537	0.500	0.500	0.482	0.494	14
0.653	0.980	0.941	0.347	0.020	0.059	0.590	0.962	0.894	0.815	1
0.347	0.020	–0.014	0.653	0.980	1.014	0.434	0.338	0.330	0.367	29
0.500	0.500	0.463	0.500	0.500	0.537	0.500	0.500	0.482	0.494	14
0.206	0.413	0.271	0.794	0.587	0.729	0.386	0.460	0.407	0.418	27
0.972	0.480	0.453	0.028	0.520	0.547	0.946	0.490	0.478	0.638	6
0.000	0.877	0.799	1.000	0.123	0.201	0.333	0.803	0.713	0.617	7
0.500	0.500	0.463	0.500	0.500	0.537	0.500	0.500	0.482	0.494	14
0.613	0.464	0.503	0.387	0.536	0.497	0.564	0.483	0.502	0.516	13
0.322	0.607	0.665	0.678	0.393	0.335	0.425	0.560	0.599	0.528	12
0.706	0.036	–0.064	0.294	0.964	1.064	0.630	0.341	0.320	0.430	25
0.500	0.500	0.463	0.500	0.500	0.537	0.500	0.500	0.482	0.494	14
0.528	0.143	0.137	0.472	0.857	0.863	0.515	0.368	0.367	0.417	28
1.000	0.123	0.127	0.000	0.877	0.873	1.000	0.363	0.364	0.576	11
0.028	0.520	0.473	0.972	0.480	0.527	0.340	0.510	0.487	0.446	23
0.681	0.623	0.615	0.319	0.377	0.385	0.611	0.570	0.565	0.582	10
0.472	0.857	0.789	0.528	0.143	0.211	0.486	0.778	0.703	0.656	4
0.294	0.964	0.991	0.706	0.036	0.009	0.415	0.933	0.982	0.777	2
0.794	0.587	0.654	0.206	0.413	0.346	0.708	0.548	0.591	0.616	8
0.359	0.893	0.749	0.641	0.107	0.251	0.438	0.824	0.666	0.643	5
0.819	0.000	–0.025	0.181	1.000	1.025	0.734	0.333	0.328	0.465	21
0.387	0.536	0.423	0.613	0.464	0.577	0.449	0.519	0.464	0.477	20
0.859	0.516	0.413	0.141	0.484	0.587	0.780	0.508	0.460	0.583	9
0.319	0.377	0.311	0.681	0.623	0.689	0.423	0.445	0.420	0.430	26
0.500	0.500	0.463	0.500	0.500	0.537	0.500	0.500	0.482	0.494	14
0.678	0.393	0.262	0.322	0.607	0.738	0.608	0.452	0.404	0.488	19
0.141	0.484	0.513	0.859	0.516	0.487	0.368	0.492	0.506	0.456	22

S_s	D_c	F_r	C_fr
2000	0.2	750	6

OPTIMIZATION TOOL	S_s	D_c	F_r	C_fr	PREDICTED	ACHIEVED	PREDICTED	ACHIEVED	PREDICTED	ACHIEVED
OPTIMIZATION TOOL	S_s	D_c	F_r	C_fr	S_r		M_rr		P_c
BOX BEHNKEN - SINGLE OBJECTIVE
S _r	2000	0.6	750	6	1.524	1.568	–	–	–	–
M _rr	1750	0.6	750	8	–	–	1.3328	1.4335	–	–
Deviation					S _s = 0.044		M _rr = 0.101		–	–
% Deviation					S _s = 2.89		M _rr = 7.55		–	–
MULTI-OBJECTIVE
P _c	1750	0.2	500	6	4.207	3.807	0.2351	0.2678	0.205	0.231
Deviation					S _s = 0.4		M _rr = 0.0327		P _c = 0.026
% Deviation					S _s = 9.5		M _rr = 13.91		P _c = 12.68
GRA – MULTI OBJECTIVE
S _s /M _rr /P _c	2000	0.2	750	6	2.844	2.867	0.262	0.278	0.2171	0.2201
Deviation					S _s = 0.023		M _rr = 0.016		P _c = 0.003
% Deviation					S _s = 0.81		M _rr = 6.1		P _c = 1.38

Laboratório de Hidrogênio, Coppe - Universidade Federal do Rio de Janeiro, em cooperação com a Associação Brasileira do Hidrogênio, ABH2 Av. Moniz Aragão, 207, 21941-594, Rio de Janeiro, RJ, Brasil, Tel: +55 (21) 3938-8791 - Rio de Janeiro - RJ - Brazil
E-mail: revmateria@gmail.com

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[1] Corresponding Author: R. Suresh Kumar.

Standard Deviation	0.0003879	R²	0.999998
Mean	3.43	Adj R²	0.999997
C.V.%	0.0833287	Pred R²	NA

Standard Deviation	0.0006958	R²	0.987379
Mean	0.57664	Adj R²	0.987747
C.V.%	0.0755287	Pred R²	NA
PRESS	NA	Adequate Precision	2342.5029

Standard Deviation	0.0016565	R²	0.984776
Mean	0.57544	Adj R²	0.959264
C.V.%	0.0866287	Pred R²	NA
PRESS	NA	Adequate Precision	3152.5149