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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.

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.

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.

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.

The purpose of this review paper is to define the dominating factors (such as fiber, yarn, fabric structure, sewing thread, sewing needle and machine parameters) that affect the seam damages and causing defects. It also describes the various explanations of sewing defects in garment production and critically analyzes them for optimum selection of parameters and speeds for minimizing such faults. Hence, the knowledge of various factors which affect the sewing damages/defects will be helpful for garment manufacturers/researchers to know influence of the parameters and control the quality of producing seam.

This section is not applicable for a review paper.

Sewing damages such as needle cut and other sewing damages/defects are studied mostly in woven fabric. There are very few studies conducted on knitted fabric sewing damages/defects. The sewing damage problems do not have single solution that is capable of removing these damages in fabric. All the determined and affecting parameters related to fiber, yarn, fabric construction, sewing thread and sewing machine must be examined to design appropriate remedial measurement related to machine design, fabric parameters and sewing thread. This could help in minimizing or eliminating the needle cut and other sewing damage problems.

It is an original review work and is helpful for garment manufacturers/researchers to reduce the defects and be able to produce good quality seam.

This paper aims to develop at sewing thread during the seam formation may lead to the compression of fabric under seam. In the present study, the model has been proposed to predict the seam compression and calculation of seam boldness, as well as thread consumption by considering seam compression.

The effect of sewing parameters on the fabric compression at the seam (Cf) for fabrics of varying bulk density was studied by the Taguchi method and also the multilinear regression equation is obtained to predict seam compression by considering these parameters. The framework has been set as per the single view metrology approach to measuring structural seam boldness (Bs). One of the basic geometrical models (Ghosh and Chavhan, 2014) for the prediction of thread consumption at lock stitch has been modified by considering fabric compression at the seam (Cf).

The multilinear regression model has been proposed which can predict the compression under seam using easily measurable fabric parameters and stitch density. The seam boldness is successfully calculated quantitatively using the proposed formula with a good correlation with the seam boldness rated subjectively. The thread consumption estimation from the proposed approach was found to be more accurate.

The compression under seam is found out using easily measurable parameters; fabric thickness, fabric weight and stitch density from the proposed model. The attempt has been made to calculate seam boldness quantitatively and the new approach to find out thread consumption by considering the seam compression has been proposed.

This 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.

To 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.

According 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.

This 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).

Until 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 purpose of this review paper is to outline the parachute materials and its behavior. To enhance parachute life, it is highly desirable to consider the commercial angle for any parachute manufacturing industry and its components under varying operational conditions. Hence, the knowledge of various textile materials and operational conditions which contributes the parachute strength and durability will be helpful for industries/researchers.

This section is not applicable for a review paper.

Parachute is a material used in numerous real-time applications such as man-drop, cargo delivery, aircraft recovery and aircraft decelerator which drastically reduces human efforts and time. However, each application requires a unique design and fabric selection to achieve the area of drag needed and the terminal velocity of the parachute material while in flight. For designing a man-drop parachute, the most critical parameters are weight and strength which must be considered during manufacturing. The army person uses the man-drop parachute, which must be as light as possible.

This paper is an original review work and will be helpful for parachute manufacturers/researchers to enhance the life of parachutes with improved functionality.

Motorcycle is one of the popular modes of transport in developing countries. However, the statistics related to accidents show that motorcycles are the most vulnerable vehicles. Research studies have revealed that half of all the possible types of motorcycle injuries could be reduced or prevented using effective protective clothing. Facts and figures emphasize that this is high time to develop a safety jacket for motorbike riders. This paper aims to develop an innovative, integrated automatic air-inflated tubeless jacket to prevent major injuries in fatal accidents.

Two accelerometers integrated near the front axle, an angle sensor and the electronic control unit (ECU) were used to detect the collision or accident. The sensors were fixed on the bike and connected with the ECU via a bluetooth device that was always at the activated stage. The fused sensors were emulated with the ECU under laboratory conditions. The trigger signal generated by the crash discriminant algorithm triggered the chemical reaction to generate N2 gas and inflate the tubeless safety jacket.

Under laboratory conditions, it was found that the signal generated by the ECU unit ejected approximately 15 litres of N2 gas in volume to fill the jacket within 100 milliseconds, which was less than the approximate estimated falling time of the rider 120 milliseconds.

The existing developments of airbag systems in motorbikes are mounted on the motorbikes' frame, following the airbag systems in automobiles. These developments cannot fully protect the rider due to differentiation in crash dynamics and respective positions of the rider at the point of impact. Though few safety jackets and airbag vests are developed, the airbag deployment is activated when rider and motorbike separated during a collision using a tether-triggering mechanism. The authors designed the jacket so that inflation is activated not only by crash sensors but also on the fusion of multiple sensors based on a crash discriminative algorithm. The airbag deployment mechanism is incorporated with the jacket and acts as a safety jacket during a collision.

Simulating the behavior of clothing has always been of interest in the apparel, fashion and computer game industries. With the development of these industries, there is a need to increase the accuracy of clothing simulation techniques. A garment contains many seams whose behavior affects its final appearance. In this study, a numerical model is presented to simulate seam puckers in single- and double-layer fabrics.

A yarn-level simulation technique has been used for this purpose. Based on this technique, the individual threads in the fabric structure and the sewing threads are modeled separately. Then, their behavior and interaction with each other are considered in the seam pucker model.

The model is used to simulate the real samples. The results show that the proposed model is able to simulate the degree of seam puckering for a single-layer fabric with an average error of 7.9% and for a double-layer fabric with an average error of 8.5%.

The behavior of the seam is affected by the properties, behavior and interaction of the sewing threads and yarns in the fabric structure. In previous studies, the parameters related to seams and fabrics were not fully considered. In this study, a new yarn-level model is presented to simulate seam puckering in woven fabrics. The most important advantage of this type of simulation is the ability to examine the interaction of fabric threads as well as the interaction of sewing threads with each other and with the threads of the fabric structure.

This paper aims to develop a single regression model (instead of developing models separately for each thread type) to predict the sewing thread consumption for cotton and polyester staple spun threads.

A single regression model is developed for predicting sewing thread consumption for cotton and polyester threads. The polyester sewing threads have lower sewing thread consumption as compared to cotton threads because of their higher elongation behaviour. The model differentiates between the cotton and polyester sewing threads using their elongation values at peak levels of tensions experienced by the sewing threads during stitch tightening. By comparing the estimated thread consumption values with actual values, the effectiveness of model is evaluated with root mean square error and coefficient of determination (R²).

During the sewing process, by understanding the behaviour of different types of sewing threads, it is possible to develop a single regression model for all types of threads.

The sewing thread consumption can be easily calculated for cotton and polyester sewing threads using a single regression equation using the sewing assembly thickness, stitch density and elongation of thread at peak tension. The garment manufacturers need not depend on different charts for sewing thread consumption for stock management.

The sewing thread consumption is different for different types of threads, and garment manufacturers have to depend on different charts given by sewing thread manufacturers or use different equations for each type of threads. Using this single regression equation, sewing thread consumption for cotton and polyester sewing thread can be estimated accurately.

Preparing a collection is a costly process when compared with mass production. Although it is a short‐run process, there are high unit costs due to the need for ordering knitted or woven material, and making necessary preparations for print and stitch patterns. All of these factors increase the cost of collection samples. Because the amount of material to be used for the preparation of the collection is small, having the material knitted or woven, or having it dyed, if it is raw, is more expensive compared with mass production. However, if the company trusts its own collection and receives positive feedback from its customers, it may consider using the material for production and keep the orders at a high level, thus decreasing the unit price. The purpose of this study is to put forward methods of calculating expenses for preparing a collection and collection costs. It also presents a sample application using present‐day values to give a comparison of collection samples with mass production samples. This makes it possible to come up with the most profitable production techniques. For this purpose, 40 T‐shirt designs were made; of these, ten models with different production features were carefully selected. Production steps were classified and the cost for each step was then calculated. By adding the cost of each step together, unit model cost was found. The collection cost was determined in the same way. It has been observed that the costs increase according to the raw material, auxiliary equipment, accessories, side processes and labour.

The aim of the present study is to consider the influence of the tensile behavior of fabric and sewing thread on the seam appearance.

In this study, the formation of seam puckering on two elastic and normal woven fabrics was explored. In order to prepare samples, various sewing threads were applied. Test specimens were sewn under five different thread tension levels. Then the appearance of samples was evaluated subjectively to determine their seam puckering grade before and after the laundering process.

The obtained outcomes of this study present that although sewing thread tension increment decreases the seam pucker ranking in the similar sewing condition, elastic fabrics have a greater seam pucker grade compared to the normal fabric due to the fabric extension and contraction during sewing and after sewing process, respectively. In addition, the elastic strain of the sewing thread is the key factor that determined sewing thread's tendency to make seam puckering. Moreover, the laundry process due to the relaxation of the sewing thread decreases the seam pucker grade.

The consistency of the tensile property of fabric and sewing thread is a crucial parameter in improving the seam appearance and obtaining a smooth seam.