Assessment of jeans sewing thread consumption by applying metaheuristic optimization methods

PurposeThis work highlights the optimization of the consumed amount of sewing thread required to make up a pair of jeans using three different metaheuristic methods; particular swarm optimization (PSO), ant colony optimization (ACO) and genetic algorithm (GA) techniques. Indeed, using metaheuristic optimization techniques enable industrialists to reach the lowest sewing thread quantities in terms of bobbins per garments. Besides, the compared results of this research can obviously prove the impact of each input parameter on the optimization of the sewing thread consumption per pair of jeans.Design/methodology/approachTo assess objectively the sewing thread consumption, the optimized sewing conditions such as thread composition, needle size and fabric composition are investigated and discussed. Hence, a Taguchi design was elaborated to evaluate and optimize objectively the linear model consumption. Thanks to its principal characteristics and popularity, denim fabric is selected to analyze objectively the effects of studied input parameters. In addition, having workers with same skills and qualifications to repeat each time the same sewing process will involve having the same sewing thread consumption values. This can occur in some levels such as end of sewing, the number of machine failures, the kind of failure and its complexity, the competency of the mechanic and his way to repair failure, the loss of thread caused by threading and its frequency. Seam repetition due to operator lack of skill will obviously affect clothing appearance and hence quality decision. Interesting findings and significant relationship between input parameters and the amount of sewing thread consumption are established.FindingsAccording to the comparative results obtained using metaheuristic methods, the PSO and ACO technique gives the lowest values of the consumption within the best combination of input parameters. The results show the accuracy of the applied metaheuristic methods to optimize the consumed amount needed to sew a pair of jeans with a notable superiority of both PSO and ACO methods compared to experimental ones. However, compared to GA method, ACO and PSO algorithms remained the most accurate techniques allowing industrials to minimize the consumed thread used to sew jeans. They can also widely optimize and predict the consumed thread in the investigated experimental design of interest. Consequently, compared to experimental results and regarding the low error values obtained, it may be concluded that the metaheuristic methods can optimize and evaluate both studied input and output parameters accurately.Practical implicationsThis study is most useful for denim industrial applications, which makes it possible to anticipate, calculate and minimize the high consumption of sewing threads. This paper has not only practical implications for clothing appearance and quality but also for reduction in thread wastage occurring during shop floor conditions like machine running, thread breakage, repairs, etc. (Kawabata and Niwa, 1991). Unless the used sewing machine is equipped within a thread trimmer improvement in garment seam appearance cannot be achieved. By comparing and analyzing the operating activities of the regular lock stitch 301 machine with and without a thread trimmer, a difference in time processing can be grasped (Magazine JUKI Corporation, 2008). Time consumed in trimming by a lockstitch machine without a thread trimmer equals 3.1 s compared to 2.6 s by a thread trimming one. Hence, the reduction rate in the time processing equals 16.30%. This paper aimed to implement the optimal consumption (thread waste outstanding number of trials). Unless highly skilled workers are selected and well-motivated, the previous recommended changes will not be applied. The saved cost of the sewing thread reduction can be used to buy a better quality of fabric and/or thread. However, these factors are not always the same as they can vary according to customer's requirements because thread consumption is never a standard for sewn product categories such as trousers, shirts and footwear (Khedher and Jaouachi, 2015).Originality/valueUntil now, there is no work dealing with the investigation of the metaheuristic optimization of the consumed thread per pair of jeans to minimize accurately the amount of sewing thread as well as the sewing thread wastage. Even though these techniques of optimization are currently in full development due to some advantages such as generality and possible application to a large class of combinatorial and constrained assignment problems, efficiency for many problems in providing good quality approximate solutions for a large number of classical optimization problems and large-scale real applications, etc., are not applied yet to decrease sewing thread consumption. Some recent published works used statistical techniques (Taguchi, factorial, etc.), to evaluate approximate consumptions; conversely, other geometrical and mathematical approaches, considering some assumptions, used stitch geometry and remained insufficient to give the industrialists an implemented application generating the exact value of the consumed amount of sewing thread. Generally, in the clothing field 10–15% of sewing thread wastage should be added to the experimental approximate consumption value. Moreover, all investigations are focused on the approximative evaluations and theoretical modeling of sewing thread consumption as function of some input parameters. Practically, the obtained results are successfully applied and the ACO method gives the most accurate results. On the other hand, in the point of view of industrialists the applied metaheuristic methods (based on algorithms) used to decrease the amount of consumed thread remained an easy and fruitful solution that can allow them to control the number of sewing thread bobbin per garments.

The parameters affecting seam quality: a comprehensive review

Purpose The conversion of fabric into a garment involves many interactions such as the selection of suitable sewing thread, optimization of sewing parameters, ease of conversion of fabric into the garment and actual performance of the sewn fabric during wear of the garment. The adjustment of all sewing parameters is necessary to ensure quality. The purpose of this paper is to define the parameters that affect seam quality comprehensively. Design/methodology/approach This study primarily focuses on the studies dealing with the effect of various parameters on-seam quality in detail. A systematic literature review was conducted. Findings The interactions between parameters may lead to different results than the effect of a single parameter. In addition, changing some parameters may have a positive effect on one element of seam quality while having a negative effect on another. For this reason, it is very important to properly select the parameters according to the specific end use of the garment products and also to consider the interactions. Originality/value The knowledge of various factors that affect seam quality will be helpful for manufacturers to improve production performance and to be able to produce high-quality seam.

Artificial neural network and regression models for prediction of sewing thread consumption for multilayered fabric assembly at lockstitch 301 seam

Purpose This paper aims to propose the artificial neural network (ANN) and regression models for the estimation of the thread consumption at multilayered seam assembly stitched with lock stitch 301. Design/methodology/approach In the present study, the generalized regression and neural network models are developed by considering the fabric types: woven, nonwoven and multilayer combination thereof, with basic sewing parameters: sewing thread linear density, stitch density, needle count and fabric assembly thickness. The network with feed-forward backpropagation is considered to build the ANN, and the training function trainlm of MATLAB software is used to adjust weight and basic values according to the optimization of Levenberg Marquardt. The performance of networks measured in terms of the mean squared error and the layer output is set according to the sigmoid transfer function. Findings The proposed ANN and regression model are able to predict the thread consumption with more accuracy for multilayered seam assembly. The predictability of thread consumption from available geometrical models, regression models and industrial empirical techniques are compared with proposed linear regression, quadratic regression and neural network models. The proposed quadratic regression model showed a good correlation with practical thread consumption value and more accuracy in prediction with an overall 4.3% error, as compared to other techniques for given multilayer substrates. Further, the developed ANN network showed good accuracy in the prediction of thread consumption. Originality/value The estimation of thread consumed while stitching is the prerequisite of the garment industry for inventory management especially with the introduction of the costly high-performance sewing thread. In practice, different types of fabrics are stitched at multilayer combinations at different locations of the stitched product. The ANN and regression models are developed for multilayered seam assembly of woven and nonwoven fabric blend composition for better prediction of thread consumption.

Effects of Laundering on Seaming Attributes of a Fabric for Public Basic school uniforms

Quality seams have great impact on the quality of garments. The study evaluated effect of laundering on plain seam in a suitable fabric for Ghanaian public basic school uniforms by varying sewing thread and stitch density to establish suitable ones for the achievement of quality in school uniforms. A total number of 290 specimens were used. Washed specimens were subjected to three cycles of washing using Launder-Ometer (Gyrowash 315) and seam strength, efficiency and elongation were evaluated. Means of the attributes were determined and analysis of variance at 0.05 alpha level are used for hypotheses testing. Sewing thread brand B in 14 stitches per inch (spi) provided greater seam strength, efficiency and elongation before and after washing.

The hemostatic and wound healing effects of autologous fibrin adhesive in experimental animals

Objectives: Evaluate the hemostasis and skin graft wound healing effect of autologous fibrin adhesive in experimental animals. Methods: experimental method, conducted on 6 rabbits to determine the role of fibrin glue in hemostasis, ability to heal skin graft in experimental rabbits through assessment of graft status: size, swelling, color, boundary between the puzzle piece and the surrounding skin. Results: 1/ Effective hemostasis: fibrin glue transplant: hemostasis time is 129.33±42.01 seconds, transplant time is 128.33±41.87 seconds; suture stitching: hemostasis time is 475.17±59.97 seconds, stitching time is 350.67±52.23 seconds. At times after 1 day, 3 days, 7 days, 10 days, 14 days, 20 days and 30 days there is an earlier phenomenon of wound healing, the flattened and scarred patch is more beautiful than the skin graft with sewing thread. Conclusions: Using autologous fibrin glue in skin grafting has the ability to stop bleeding, heal wounds and achieve better aesthetics than skin grafting with sutures.

Modeling the consumption of sewing thread for stitch class 301 through image analysis by using Fourier series

This research work aims at modeling the sewing thread consumption for stitch class 301 through image analysis by using Fourier series. A mathematical model was developed by using the geometry of stitch class 301 for calculating sewing thread consumption. The modeling of stitch class 301 was conducted by changing the stitch density and thickness of the stitched fabric. The stitch density was adjusted by varying the stitch length (2, 3 and 4 mm) and the thickness of stitched fabric (2, 3 and 4 plies of fabric).The interlacement of sewing thread between the needle and bobbin was used for the measurement of arc length of one stitch. The total consumption of sewing thread was determined by multiplying the arc length by 4. The developed model was verified by using nine different stitched samples for comparing the predicted and measured values of sewing thread consumption. This model predicted the sewing thread consumption with 95% accuracy. Error percentages were also calculated for determining the major influencing factors that affected the sewing thread consumption. The proposed model can effectively be employed in garment industries for determining sewing thread consumption.

Investigating the Performance of Sewn Seams from Woven Fabrics

Seam performance ensures the durability, attractiveness, strength etc. of the sewn garments. Fabric types, fabric densities, fabric strength, seam types, sewing thread count, thread strength, stitch densities and stitch types influence the performance of a sewn seam. This paper investigates the performance of sewn seam of various structure of woven fabric. The variables of this research are stitch densities (SPI), sewing thread count, seam types and woven structures. We conduct our research over plain, twill 2/1, twill 2/2, oxford and poplin woven structure. Firstly, we apply lockstitch (301) to produce superimposed, lapped and bound seam on the woven sample by using thread count 27 Tex, 30 Tex, 20/2 Tex, 40 Tex, 40/2 Tex, 40/3 Tex having stitch densities (SPI) 8, 10 and 12. Then according to ASTM D1683 standard, we measured the tensile strength test and recorded the seam breaking strength (N). We apply error bars over each diagram to investigate the standard deviation. Finally, we discuss four hypothesis to conclude our research work. We found higher seam breaking strength with the increase of thread count and SPI. The bound seam samples has shown superior seam breaking strength than superimposed and lapped seam; poplin structure surpassed to others. The seam efficiency of the samples varies from 60–90% and do not exceeds 100%. Finally, we found some significant alternatives hypothesis of the population since F values exceeded F critical values for the sewn seam.

Thread Type and Stich Density Suitable for Seams in Ghanaian Public Basic School Uniforms

Background: The serviceability of a sewn garment is influenced by the quality of its seams, which form the basic structural element. The factors that affect seam quality in garments includes, sewing thread type and stitch density. Their right choice helps with the achievement of quality seams in garments. However, the choice of suitable sewing threads and stitch densities for particular fabrics can only be determined through testing. Objective: Problems associated with poor performance of school uniforms include seam failure. The aim of the study was to determine sewing thread brand and stitch density suitable for seams for a selected fabric (79% polyester and 21% cotton) for Ghanaian public basic school uniforms. Method: A 2×3 factorial design was employed which involved two brands of sewing threads labelled Aʹ and Bʹ and three range of stitch density, 10, 12 and 14. The total number of specimens prepared from the selected fabric was 81. The parameters investigated included fabric weight, strength and elongation, seam strength, seam elongation and efficiency. The data were analysed using the Predictive Analytic Software (SPSS). Means and standard deviations of the fabric’s yarn count, weight, strength, elongation and the linear density of the sewing threads were determined. Analysis of Variance and Independent samples t-test at 0.05 alpha levels were employed in testing the hypotheses. Results: Differences for seam strength, efficiency and elongation were significant for the two sewing thread brands and the three stitch densities in both warp and weft directions of the fabric sample. The sewing thread brand B′ with stitch density 14 performed best in terms of seam strength, elongation and efficiency. Conclusion: The sewing thread brand B′ and stitch density 14 are recommended to be used for the construction of uniforms with the selected fabric to achieve quality in uniforms.