Hybrid Incremental Forming: Investigation on Localized …
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2.3 Experimental Plan
The experimental campaign in present work is planned using Taguchi’s orthogonal arrays. Two sets of experiments are performed using Taguchi’s L 25 orthogonal
array. Thickness along the formed surface is measured using Mitutoyo’s digital point
micrometre. Minimum thickness is used as response for statistical analysis.
3 Result and Discussion
The statistical tool analysis of variance (ANOVA) is used for the analysis of experimental results. Results are analysed using Minitab software at 95% confidence
level. Table 3 is ANOVA table for minimum thickness (T min ). ANOVA table decomposes the variability and significance of the process parameters. It is observed that
preforming depth is significant process parameter affecting minimum thickness in
formed parts.
3.1 Influence of Factors on Minimum Thickness (T min )
Figure 3a shows the main effects plot of thickness with respect to preforming depth.
It is observed that increase in amount of preforming depth reduces the wall thickness (T min ). As preforming is a plastic deformation process, it results in thickness
reduction. Hence, increase in amount of preforming further reduces the wall thickness as depicted in Fig. 3a. In the second stage of HISF process, SPIF process forms
the final part shape, which further reduces the thickness in formed part. Hence as
amount of stretching increases, considerable reduction in wall thickness of formed
part is observed. Figure 3b depicts the graph of preforming depth versus S/N ratio.
It is observed from the graph that small preforming depth results in high S/N ratio
ensuring better product quality (in this case improved thinning).
Table 3 ANOVA table for T min
Source
DF
Seq SS
Adj SS
Adj MS
F
P
Preforming depth (P d )
4
0.020496
0.020496
0.005124
39.21
0.000
Pitch
4
0.001096
0.001096
0.000274
2.10
0.144
Tool diameter
4
0.000456
0.000456
0.000114
0.87
0.508
Residual error
12
0.001568
0.001568
0.000131
Total
24
0.023616
R 2 = 0.934; Adjusted R 2 = 0.867
155
2.3 Experimental Plan
The experimental campaign in present work is planned using Taguchi’s orthogonal arrays. Two sets of experiments are performed using Taguchi’s L 25 orthogonal
array. Thickness along the formed surface is measured using Mitutoyo’s digital point
micrometre. Minimum thickness is used as response for statistical analysis.
3 Result and Discussion
The statistical tool analysis of variance (ANOVA) is used for the analysis of experimental results. Results are analysed using Minitab software at 95% confidence
level. Table 3 is ANOVA table for minimum thickness (T min ). ANOVA table decomposes the variability and significance of the process parameters. It is observed that
preforming depth is significant process parameter affecting minimum thickness in
formed parts.
3.1 Influence of Factors on Minimum Thickness (T min )
Figure 3a shows the main effects plot of thickness with respect to preforming depth.
It is observed that increase in amount of preforming depth reduces the wall thickness (T min ). As preforming is a plastic deformation process, it results in thickness
reduction. Hence, increase in amount of preforming further reduces the wall thickness as depicted in Fig. 3a. In the second stage of HISF process, SPIF process forms
the final part shape, which further reduces the thickness in formed part. Hence as
amount of stretching increases, considerable reduction in wall thickness of formed
part is observed. Figure 3b depicts the graph of preforming depth versus S/N ratio.
It is observed from the graph that small preforming depth results in high S/N ratio
ensuring better product quality (in this case improved thinning).
Table 3 ANOVA table for T min
Source
DF
Seq SS
Adj SS
Adj MS
F
P
Preforming depth (P d )
4
0.020496
0.020496
0.005124
39.21
0.000
Pitch
4
0.001096
0.001096
0.000274
2.10
0.144
Tool diameter
4
0.000456
0.000456
0.000114
0.87
0.508
Residual error
12
0.001568
0.001568
0.000131
Total
24
0.023616
R 2 = 0.934; Adjusted R 2 = 0.867