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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.

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.

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.

This paper aims to provide a rapid and accurate method to predict the amount of sewing thread required to make up a garment.

Three modeling methodologies are analyzed in this paper: theoretical model, linear regression model and artificial neural network model. The predictive power of each model is evaluated by comparing the estimated thread consumption with the actual values measured after the unstitching of the garment with regression coefficient R² and the root mean square error.

Both the regression analysis and neural network can predict the quantity of yarn required to sew a garment. The obtained results reveal that the neural network gives the best accurate prediction.

This study is interesting for industrial application, where samples are taken for different fabrics and garments, thus a large body of data is available.

The paper has practical implications in the clothing and other textile‐making‐up industry. Unused stocks can be reduced and stock rupture avoided.

The results can be used by industry to predict the amount of yarn required to sew a garment, and hence enable a reliable estimation of the garment cost and raw material required.

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.

Studies the effect of thread tension and the stitch length, L, as well as the fabric thickness, t, and its compressive modulus, E, on the seam balance and total thread consumption.

The mechanism of strength reduction of sewing threads has been discussed in Part I of this paper. The effect of fabric tightness and certain thread properties like its size, coefficient of yarn‐metal friction, twist direction, number of piles, type of fibre and fibre denier on strength reduction has been studied and found to influence the severity of strength reduction of the thread.

In Part 1 of this series of papers, we investigated the importance of fabric overfeed in the sewing operations during tailoring. It was also shown how fabric bending rigidity, formability, shear and hygral expansion are important in clothing manufacture. The present paper is concerned with the measurement and experimental study of seam balance, breaking elongation and bending properties of seams. The aim is to evaluate quantitatively the consumption of sewing thread and the relationship between the degree of fabric overfeed during sewing and the curvature of the seam in the garment. Balanced seams have much higher breaking elongation and more symmetrical bending properties than unbalanced seam structures. A natural curvature and curling couple result from fabric overfeed during sewing. The value of the curvature is time‐dependent because of fabric viscoelastic effect and also depends on the level of fabric overfeed, the tensile and longitudinal compressive module of the component fabrics and the structure of the seamed composite. The natural curvature of the seam may be derived quantitatively from the relative lengths of overfeed fabrics using a modified theory for a bimetallic themostatic strip.

The purpose of this paper is to establish the regression equation based upon a set of samples prepared through structured design of experiment and form a prediction model for prediction of the areal density gram per square meter (GSM) of the embroidered fabrics and study the influence of basic input parameters.

Embroidery samples are prepared taking input parameters as GSM of the base fabric, linear density of the embroidery thread and stitch density of the embroidery design. Three levels of values are identified for each of the input parameters. Taguchi and Box-Behnken experiment design principles are used to prepare two sets of samples. Linear multiple regression is used to determine the prediction equations based upon each of the two sets and the combined set as well. Prediction equations are statistically verified for the prediction accuracy. Also, surface curves are prepared to study the influence of embroidery parameters on the GSM.

It is found that all the three prediction models developed in this study can predict with a very satisfactory level of accuracy. However, the regression equation based upon the data set prepared according to Taguchi experiment design is emerged as the prediction model with highest level of prediction accuracy. Corresponding equation coefficients and several three-dimensional surface curves are used to study the influence of embroidery parameters and it is found that the stitch density is the most influential input parameter followed by stitch length and the GSM of base fabric.

This can be used to assess the GSM of embroidered fabrics before starting the actual embroidery process. So, this model can help the embroidery designers significantly to pre-estimate the GSM of the embroidered fabrics and select the design parameters accordingly. Also, this model can be a useful tool for estimation of thread consumption and thread cost in embroidery.

The input parameters used here are very basic parameters related to design and materials, which can be easily available. And also, a simple linear multiple regression is used to make the prediction equation simple and easy to use. So, this model can help the embroidery designers or garment designers to select/adjust the embroidery parameters and thread parameters accordingly in the planning and designing stage itself to ensure that the GSM of embroidered fabrics remains within desirable range. Also, this prediction model developed hereby may be a very useful tool for estimation of the consumption and cost of embroidery threads.

This paper presents a very fundamental study to reveal the effect of embroidery parameters on the GSM, through development of regression equations. It can help future researchers in optimizations of input parameters and forming a technical guideline for the embroidery designers for selection of the design parameters for a desired GSM of embroidered fabric.

In this paper, the contribution of dynamic loading, needle and fabric, and the bobbin thread interaction on the changes in the tensile properties of the needle thread are to be investigated.

Tensile properties of the needle thread have been studied at four sewing stages, namely before being subjected to any loading, after dynamic loading, before bobbin thread interaction and after sewing.

It is observed that bobbin thread interaction plays a dominant role in the reduction of tensile properties except breaking elongation in cotton threads. Dynamic loading is mainly responsible for reduction in the breaking elongation of cotton threads. During sewing, there is an increase in initial modulus due to the dynamic loading, which is more in the case of cotton threads than polyester threads. However, the impact of dynamic loading on tenacity, breaking elongation and breaking energy is greater for coarser cotton thread. The contribution of bobbin thread interaction is more for fine threads as compared to coarse threads.

Since seam strength is dependent on the thread strength, a reduction in thread strength during sewing will lead to lower seam strength than expected. Therefore, in order to minimize the thread strength reduction, it is important to understand the contribution of different machine elements or processes during sewing. During high‐speed sewing, the dynamic and thermal loading are found to be the major causes of strength reduction of needle thread, which can go up to 30‐40 per cent. However, the extent of strength loss at different sewing stages is unknown.

The study will help in engineering sewing threads, designing of sewing machines and selection of process parameters for controlling loss of useful properties of sewing threads.