84
V. Sisodia et al.
5 sheet metal can be improved with local laser heating system. Rauch et al. [16]
investigated the influence of tool path strategy, feed rate, and axial increment on
profile accuracy, forming force and maximum forming depth of formed part. They
proposed a dedicated tool path technique to reduce the geometric error. Fiorentino
et al. [17] analyzed the influence of step decrements on forming forces, formability,
geometric deviation and thinning in SPIF process, and two-point incremental forming
(TPIF) process. Good geometric accuracy was observed in parts formed with TPIF
process. Radu et al. [18] studied the effect of process parameters on residual stresses
and profile deviation of formed parts. They reported that large tool diameter and
pitch result in poor geometric accuracy whereas large feed rate and spindle speed
form accurate parts. Residual stresses increase with increase in pitch and feed rate.
Behera et al. [19] proposed a methodology to map the influence of different factors
on the geometric error of part formed by SPIF process. Partial tool path strategy
that records the geometric error of particular feature in the formed part was also
presented. It was reported that the maximum geometrical deviation in the formed part
is significantly reduced by optimized tool path strategy. Lu et al. [20] reported that
geometric accuracy increases by decreasing the step size. Lu et al. [21] developed a
model predictive control algorithm to compensate the tool position in both horizontal
and vertical directions in SPIF and TPIF process to reduce the geometric error of
formed component. The developed algorithm is able to reduce the geometric error.
Wang et al. [22] investigated the influence of tool path strategies, namely squeezing
and reverse bending in double-sided incremental forming process (DSIF) to improve
the geometric accuracy of parts. They reported that spring-back in DSIF technique is
reduced using reverse bending and squeezing. Researchers have reported that poor
geometric accuracy in SPIF process is due to minimum constraints on the blank sheet.
The main reasons for high geometric error in SPIF process are absence of supporting
die, spring-back (combined local and global), bending of sheet at the clamped edges,
and presence of residual stress in formed part. To improve the geometric accuracy
of parts, methods such as stress relieving techniques [19, 23–25] and use of partial
support or double-sided incremental forming [25, 26] were used. From the critical
review of literature, it is clear that very less research efforts have been made to
study the geometric error of formed part in SPIF process. Different strategies have
been proposed by researchers to improve geometric error but most of them is either
very complex in nature or time consuming. Also, no work has been reported in
studying geometric error in SPIF process with dummy sheet. Therefore, objective
of present work is to investigate the effect of process parameter on geometric error
(RMSE), develop mathematical model, and optimize significant process parameters
to minimize RMSE.
V. Sisodia et al.
5 sheet metal can be improved with local laser heating system. Rauch et al. [16]
investigated the influence of tool path strategy, feed rate, and axial increment on
profile accuracy, forming force and maximum forming depth of formed part. They
proposed a dedicated tool path technique to reduce the geometric error. Fiorentino
et al. [17] analyzed the influence of step decrements on forming forces, formability,
geometric deviation and thinning in SPIF process, and two-point incremental forming
(TPIF) process. Good geometric accuracy was observed in parts formed with TPIF
process. Radu et al. [18] studied the effect of process parameters on residual stresses
and profile deviation of formed parts. They reported that large tool diameter and
pitch result in poor geometric accuracy whereas large feed rate and spindle speed
form accurate parts. Residual stresses increase with increase in pitch and feed rate.
Behera et al. [19] proposed a methodology to map the influence of different factors
on the geometric error of part formed by SPIF process. Partial tool path strategy
that records the geometric error of particular feature in the formed part was also
presented. It was reported that the maximum geometrical deviation in the formed part
is significantly reduced by optimized tool path strategy. Lu et al. [20] reported that
geometric accuracy increases by decreasing the step size. Lu et al. [21] developed a
model predictive control algorithm to compensate the tool position in both horizontal
and vertical directions in SPIF and TPIF process to reduce the geometric error of
formed component. The developed algorithm is able to reduce the geometric error.
Wang et al. [22] investigated the influence of tool path strategies, namely squeezing
and reverse bending in double-sided incremental forming process (DSIF) to improve
the geometric accuracy of parts. They reported that spring-back in DSIF technique is
reduced using reverse bending and squeezing. Researchers have reported that poor
geometric accuracy in SPIF process is due to minimum constraints on the blank sheet.
The main reasons for high geometric error in SPIF process are absence of supporting
die, spring-back (combined local and global), bending of sheet at the clamped edges,
and presence of residual stress in formed part. To improve the geometric accuracy
of parts, methods such as stress relieving techniques [19, 23–25] and use of partial
support or double-sided incremental forming [25, 26] were used. From the critical
review of literature, it is clear that very less research efforts have been made to
study the geometric error of formed part in SPIF process. Different strategies have
been proposed by researchers to improve geometric error but most of them is either
very complex in nature or time consuming. Also, no work has been reported in
studying geometric error in SPIF process with dummy sheet. Therefore, objective
of present work is to investigate the effect of process parameter on geometric error
(RMSE), develop mathematical model, and optimize significant process parameters
to minimize RMSE.