A software application has been
developed in Labview allowing the acquisition and processing of the resulting
signals. The signal processing functions of this software have been reported
elsewhere [3]. The most important one is splitting the thread tension signals
into stitch cycles (each cycle corresponding to one rotation of the machine’s
main shaft) and in turn dividing each stitch cycle into phases, which are
associated to specific events of stitch formation. best sewing machine dealers in chennai. For each one of these
phases, that will be described later, features such as peak values, power,
energy or average of the signal is computed. In the current experimental work,
thread force waveforms throughout the stitch cycle are being analysed when
varying parameters such as static thread tension adjustment, number of fabric
layers, mass per unit area and thickness of fabric, needle size and sewing
speed. Both the effect of the machine settings and process variables on the
thread tensions, as well as the effect of the material properties are
investigated. In this paper, the effect of static thread tension and the
influence of the fabric on the dynamic tension signals are analysed.
The first step was to observe the
resulting thread tension signals and interpret their relation to the stitch
formation process. Some trials with the adjustment of the needle thread
pre-tensions were made.
Afterwards, a more comprehensive experiment
was set up to investigate on the influence of the material being sewn.
Three similar shirt fabrics with
different mass per unit area were used, namely
• Fabric 1 : 1x1 plain weave ;
100% cotton; 102 g/m2; thickness 0,22 mm
• Fabric 2 : 2x1 twill fabric;
100% cotton; 127 g/m2; thickness 0,23 mm
• Fabric 3 : Mixed structure;
100% cotton; 118 g/m2; thickness 0,23 mm
The machine was set-up as
following:
• Groz-Beckert 134 needle with
round point and size 8;
• 100% corespun polyester thread
with ticket number 120;
• Constant sewing speed of 2000
stitches per minute;
• Stitch length 3,5 mm
• Static thread tensions were
adjusted empirically for the fabric with average weight; no difference in
stitch alance and tightness could be observed sewing the three fabrics with
this adjustment. Adjustment was maintained unchanged throughout the experiment.
For each fabric, strips of fabric
of 10 cm width and 30 cm length were cut. Specimens with two and four layers of
these strips were prepared. On each one of them, 10 seams with 20 stitches each
were performed.
Peak values for each of the three
defined stitch cycle phases (see next section) were extracted by the developed
software. Results were compared between specimens of two and four layers. For
this purpose, the Statistical Package for the Social Sciences (SPSS 20.0) was
used. For each experiment a MANOVA (one-way between-groups multivariate
analysis of variance) was performed. Three dependent values were used: Peak
values of thread force in phase 1, 2 and 3. The independent variable was the
number of layers: 2 or 4. The analysis was carried out following the
recommendations of Pallant [16].
Preliminary assumption testing
was conducted to check for normality, linearity, univariate and multivariate
outliers, homogeneity of variance-covariance matrices, and multicollinearity,
with no serious violations noted. The results of the MANOVA analysis include
the F-statistic value, average (M), standard deviation (SD), Wilks’ Lambda,
significance level p and partial eta squared. Wilks’ Lambda is one of the most
reported statistics. If the associated significance level p is less than 0.05,
then it can be concluded that there is a significant difference between groups.
Partial eta squared, also known as effect size, shows the proportion of the
variance in the dependent variable than can be explained by the independent
variable. The guidelines proposed by Cohen [17] have been used in this work:
0.01=small effect, 0.06=moderate effect, 0.14=large effect. When the results
for the dependent variables were considered separately, a Bonferroni adjusted
alpha level of 0.017 was used. In this case, a significance level p smaller
than 0.017 represents a significant difference.
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