Finding on fabric layers/material

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. juki usha singer merritt sewing machine price list showroom in chennai

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.