Performance optimisation of the turning process along with multi-surface heating process

Run	A	B	C	D	Cutting force (N)	Surface roughness (μm)
1	Normal	0.1	0.1	50	89	3.18
2	Normal	0.125	0.2	100	84	2.84
3	Normal	0.15	0.3	150	81	3.45
4	Normal	0.175	0.4	200	88	2.97
5	IR	0.1	0.2	150	79	2.17
6	IR	0.125	0.1	200	74	2.97
7	IR	0.15	0.4	50	78	2.84
8	IR	0.175	0.3	100	76	2.34
9	UV	0.1	0.3	200	70	2.38
10	UV	0.125	0.4	150	69	2.27
11	UV	0.15	0.1	100	71	2.14
12	UV	0.175	0.2	50	73	2.17
13	HA	0.1	0.4	100	64	2.18
14	HA	0.125	0.3	50	62	2.07
15	HA	0.15	0.2	200	66	2.12
16	HA	0.175	0.1	150	68	2.01

[i] HA, hot air; IR, infrared; UV, ultraviolet

Experimental setup: (A) IR-assisted machining; (B) UV-assisted machining; and (C) HA-assisted machining.
HA, hot air; IR, infrared; UV, ultraviolet

SEM image of tool edges machined with (A) IR-assisted heating, (B) UV-assisted heating, (C) HA-assisted heating and (D) normal conditions.
HA, hot air; IR, infrared; SEM, scanning electron microscopy; UV, ultraviolet

Effect of input parameters on different parameters

SEM image of chip microstructure under (A) IR-assisted machining, (B) UV-assisted machining, (C) HA-assisted machining and (D) normal machining.
HA, hot air; IR, infrared; SEM, scanning electron microscopy; UV, ultraviolet

Effect of input parameters on surface roughness

SEM images of machined surface obtained under (A) IR-assisted machining, (B) UV-assisted machining, (C) HA-assisted machining and (D) normal machining.
HA, hot air; IR, infrared; SEM, scanning electron microscopy; UV, ultraviolet

Table 3

TOPSIS ranking

Experiment no.	$V_{i}^{+}$	$V_{i}^{-}$	J_i (preference) value)	Rank
1	0.0584	0.0313	0.3489	16
2	0.0425	0.0337	0.4425	15
3	0.0485	0.0429	0.4695	13
4	0.0404	0.0378	0.4833	11
5	0.0267	0.0503	0.6528	6
6	0.0387	0.0354	0.4779	12
7	0.0381	0.0336	0.4687	14
8	0.0399	0.0404	0.5032	10
9	0.0287	0.0462	0.6166	8
10	0.0253	0.0531	0.6775	4
11	0.0289	0.0500	0.6342	7
12	0.0230	0.0533	0.6986	3
13	0.0284	0.0594	0.6766	5
14	0.0337	0.0536	0.6136	9
15	0.0258	0.0621	0.7063	2
16	0.0243	0.0605	0.7132	1

[i] $V_{i}^{+}$ , Positive ideal solution; $V_{i}^{-}$ , negative ideal solution

[ii] TOPSIS, technique for order performance by similarity to ideal solution

Table 4

ANOVA for TOPSIS

Machining parameter	Degree of freedom	Sum of the squares	Mean square	F-value	% Contribution
A	3	0.076439	0.0255	1.308	26.16
B	3	0.048701	0.0162	0.833	16.67
C	3	0.064954	0.0217	1.112	22.23
D	3	0.088611	0.0295	1.517	30.33
Error	3	0.013443	0.0045	0.230	4.60
Total	15	0.2921	0.0195		100

[i] ANOVA, analysis of variance; TOPSIS, technique for order performance by similarity to ideal solution

Table 5

GRC and GRG values

$y_{0}^{*}$	$y_{i}^{*}$
1	1.0000	0.5517	0.6839	13
2	0.8438	0.6344	0.6853	12
3	0.7714	0.5000	0.7127	10
4	0.9643	0.6000	0.7328	9
5	0.7297	0.9000	0.7599	5
6	0.6429	0.6000	0.6619	16
7	0.7105	0.6344	0.6650	15
8	0.6750	0.8136	0.6806	14
9	0.5870	0.7956	0.7085	11
10	0.5745	0.8471	0.7534	6
11	0.6000	0.9172	0.7338	8
12	0.6279	0.9000	0.7642	4
13	0.5192	0.8944	0.7922	3
14	0.5000	0.9600	0.7343	7
15	0.5400	0.9290	0.8230	1
16	0.5625	1.0000	0.8097	2

[i] GRC, grey relational coefficient; GRG, grey relational grade

Table 6

ANOVA for GRG

Machining parameter	Degree of freedom	Sum of the squares	Mean square	F-value	% Contribution
A	3	0.618799	0.2063	0.842	16.84
B	3	0.835593	0.2785	1.137	22.73
C	3	0.933500	0.3112	1.270	25.40
D	3	0.943359	0.3145	1.283	25.67
Error	3	0.344308	0.1148	0.468	9.37
Total	15	3.6756	0.2450		100

[i] ANOVA, analysis of variance; GRG, grey relational grade

References

Authors

Metrics

Articles in this issue

DOI: https://doi.org/10.2478/msp-2022-0041 | Journal eISSN: 2083-134X (formerly 2083-124X) | Journal ISSN: 2083-1331

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Language: English

Page range: 1 - 13

Submitted on: Dec 14, 2022

Accepted on: Jan 8, 2023

Published on: Mar 3, 2023

Published by: Wroclaw University of Science and Technology

In partnership with: Paradigm Publishing Services

Keywords:

hot air,

turning,

Related subjects:

Materials sciences, other,

Nanomaterials,

Functional and smart materials,

Materials characterization and properties

© 2023 D Sathish Kumar, R Thanigaivelan, N Natarajan, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 40 (2022): Issue 4 (December 2022)

Performance optimisation of the turning process along with multi-surface heating process

Figures & Tables

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Fig. 1

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Fig. 6

Table 3

Table 4

Table 5

Table 6

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