Optimization of incremental sheet metal forming process using grey relational analysis
- The incremental sheet forming (ISF) process has features that are adaptable to a great variety of applications and demands without relying on dies and punches. However, some features of incremental sheet forming part quality can be unsatisfactory if the forming process parameters are not adeq...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP)
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/72422/ http://irep.iium.edu.my/72422/ http://irep.iium.edu.my/72422/1/72422_Optimization%20of%20Incremental%20Sheet.pdf http://irep.iium.edu.my/72422/2/72422_Optimization%20of%20Incremental%20Sheet_SCOPUS.pdf |
| Summary: | - The incremental sheet forming (ISF) process has
features that are adaptable to a great variety of applications and
demands without relying on dies and punches. However, some
features of incremental sheet forming part quality can be
unsatisfactory if the forming process parameters are not
adequately chosen. In this paper, a Taguchi-based Grey
optimization of the incremental sheet forming process is
presented for the purpose of determining a combination of
optimal process parameters that will result in a high part quality
with many favorable characteristics, such as the wall angle, the
surface roughness, and the springback. Signal-to-noise ratio
(S/N) and Taguchi’s L18 orthogonal array design were the basis
for obtaining the objective function. The impact of individual
factors on the final output was determined with Analysis of
variance (ANOVA). The study supplied the optimal process
parameters. Indeed, the vertical step depth with contribution
value of 68.5% followed by the tool diameter with 9.7%
contribution, and number of sheets with 6.1% contribution were
found to be the most influential parameters on the three
responses taken together. Consequently, the other two parameters
(spindle speed and feed rate) were deemed non-significant with
contribution of 2.9% and 1%, respectively. In addition, the
graphs and response tables that resulted from ANOVA and
Taguchi analysis together form an efficient and effective method
of finding optimal levels for each design parameter. With
optimized parameters, the ideal value of wall angle and the
minimum values of springback and surface roughness are
produced. Finally, confirmation testing, using suggested optimal
conditions, showed a GRG value with 27.4% improvement. It can
thus be concluded that the use of the multi objective optimization
of wall angle, surface roughness and springback in the proposed
Grey-Taguchi method is suitable for optimizing the ISF process
and is additionally effective for use in other metal forming
processes. |
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