Industrial Sewing Machine Control

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Industrial Sewing Machine Control

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. 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.

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The first step was to observe the resulting thread tension signals and interpret their relation to the stitch formation process. VS Enterprises 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 : 1×1 plain weave ; 100% cotton; 102 g/m2; thickness 0,22 mm

• Fabric 2 : 2×1 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.

It was found that the number of fabric layers/material thickness produces statistically significant variations when analyzing the dependent variables, Peak1, Peak2 and Peak 3, in combination, and for all three types of fabrics. As expected, and according to the stitch formation process, in all materials Peak 3 exhibits the highest values. This value has been observed to be lower in the experiments with 4 fabric layers than with two fabric layers. When analyzing the dependent variables separately, the high partial eta squared values indicate a large effect produced by the number of layers on the three force peaks. The measurement is thus able to distinguish between the number of layers presented to the machine for all fabrics and almost all thread force peaks. Surprisingly, the highest thread force peak decreases with more layers of fabric. The expectation of the team, based on the empirical knowledge on the sewing process, would be different. This shows that the quantification of sewing variables can in fact provide more detailed knowledge about the process variables and their relations, allowing development of finer control and monitoring of the machines.

A sewing test rig based on a lockstitch machine has been set up using software previously developed and used in the assessment of the operation of overlock machines. A comprehensive experiment involving materials, adjustments and needle choice is being carried out, of which some results have been shown. The relations between sewing variables are complex and experimentation is extensive due to the number of parameters involved. However, some insights are being gained that promise methods for automatic adjustment and defect detection. These are very desirable functionality for today’s textile industry, dealing with small production orders with varying materials or for technical textiles, where a more quantitative process monitoring can assure more reliable and safe products. It has been shown that the number of plies / material thickness produces statistically significant variations in thread force peaks, as expected. However, the variations are not always as expected. Further experimentation relating the static thread tension adjustment, the materials and the resulting thread forces, related to the evaluation of the seams by experienced sewing technicians and objective tests such as seam strength will shed some light on the principles of correct adjustment. This will allow the development of systems to automatically adapt the machine to new materials and to monitor the process avoiding defects and low quality seams. Other process variables such as needle penetration force and thread consumption will be included in this analysis in future work.