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

D Sathish Kumar
D Sathish Kumar
, 
R Thanigaivelan
R Thanigaivelan
 and 
N Natarajan
N Natarajan
   | Mar 03, 2023
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Published Online: Mar 03, 2023
Page range: 1 - 13
Received: Dec 14, 2022
Accepted: Jan 08, 2023
DOI: https://doi.org/10.2478/msp-2022-0041
Keywords
© 2022 Sathish Kumar D et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1

Experimental setup: (A) IR-assisted machining; (B) UV-assisted machining; and (C) HA-assisted machining.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

Fig. 2

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
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

Fig. 3

Effect of input parameters on different parameters
Effect of input parameters on different parameters

Fig. 4

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
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

Fig. 5

Effect of input parameters on surface roughness
Effect of input parameters on surface roughness

Fig. 6

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
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

GRC and GRG values

Experiment no. GRC
 GRC Rank
y0* y_0^* yi* 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

L16 orthogonal array and outcome of machining

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

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

Machining parameters and ranges

Code Description Level 1 Level 2 Level 3 Level 4
A Heating method Normal IR UV HA
B Feed rate, mm/rev 0.1 0.125 0.15 0.175
C Depth of cut, mm 0.1 0.2 0.3 0.4
D Cutting speed, m/min 50 100 150 200

TOPSIS ranking

Experiment no. Vi+ V_i^+ Vi− V_i^- Ji (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

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
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