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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.

Selection of suitable sewing needle is one of the most important parameters for ensuring an effective and fault‐free sewing process. This task requires good knowledge of basic characteristics of a sewing needle, i.e. needle type, point shape and needle fineness. Also good knowledge of sewing materials is required. The contribution presents an analysis of important parameters that influence the sewing needle selection in women’s underwear production. The importance of those parameters in ensuring the appropriate seam quality is described. The selection of a suitable sewing needle was carried out on the basis of analysis of influential sewing parameters with application of machine learning from examples.

This paper suggests a new method for determining the sewing damage that occurs in single jersey fabrics based on the calculation of the needle hole area. This paper also aims to investigate the effects of material type, sewing parameters, and repeated washing cycles on sewing damage by using this method.

Six knitted fabrics, differing in structure and raw material, were produced. Samples were sewn using different sewing parameters, and they were subjected to ten washing cycles. Values of average needle hole area, an objective indicator of sewing damage, were calculated before and after repeated washing cycles using image analysis software and were evaluated statistically.

The results showed that the average needle hole area calculated via the image analysis software effectively estimates sewing damage as the results obtained were compatible with those stated in the literature. Furthermore, fabric type, sewing direction, sewing thread type, and needle size significantly affected the sewing damage. However, stitch density did not affect the sewing damage. When the effect of washing cycles was compared, it was seen that washing leads to an increase in sewing damage.

A review of the existing literature shows that no previous study has evaluated sewing damage using image analysis software. This study proposes a novel objective method to determine the sewing damage that occurs in knitted fabrics.

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.

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.

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.

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.

Demonstrates the modelling of the kinetic process when the sewing needle enters the fabric. To get an adequate mechanical model of the given problem, the function ‐ the mathematical model ‐ of the penetration force with respect to the fabric, the needle and the mechanism in the sewing machine must be developed. The fabric was modelled as a combination of warp and weft threads. Each thread from the fabric is modelled as an ideal elastic Hook’s material. Outlines the restrictions which needed to be made to get the mathematical model of the problem.

Quantitative fabric‐needle‐sewing machine interactions at different speeds have been used to construct qualitative rules mapping fabric properties to optimum sewing machine settings for the next generation of “intelligent sewing machines”, using model free estimation. The inference procedures of fuzzy logic have been implemented in a neural network to allow for optimization of output membership functions and subsequently, self‐learning. The technique is successfully applied to industrial lockstitch and overlock sewing machines. Optimum settings were achieved under static and dynamic machine conditions from the properties of difficult fabrics and compensation for mishandling by the operator over the speed range of the sewing machine.

This paper aims to predict the needle penetration force (NPF) in denim fabrics using the artificial neural network (ANN) and multiple linear regression (MLR) models based on the effects of various sewing parameters.

In order to design the ANN and MLR models, four parameters including fabric weight, number of fabric layers, weave pattern, and sewing needle size are taken into account as the input parameters and NPF as the output parameter. According to these parameters, 140 samples of data were resulted. Each sample was tested five times. From these 140 data (input-output data pairs), 112 were used for training the ANN and MLR models and 28 samples were used to test the performance of ANN and MLR. Also, the NPF was measured on the Instron tensile tester to simulate sewing process.

The results indicated that the NPF in denim fabrics can be well predicted in terms of sewing parameters by using ANN and MLR models, in which the ANN model exhibits greater performance than MLR (RANN=0.989> RMLR=0.901).

The NPF measurement method is limited at low speed.

Using the ANN model for forecasting NPF in denim fabrics can help the garment manufactures to produce high-quality denim products and improve the sewing process through reducing seam damage. The NPF could be also measured in the cycle loading conditions similar to sewing machine process by using a special designed tools mounted on the Instron tensile tester.

Introduces a new concept which has been developed to combat the problems of sewability, named “the Sewability Integrated Environment”. It aims at knitted as well as woven materials and incorporates automated objective measurement systems to achieve automatic prediction of seam pucker, and sewing damage, on‐line optimum sewing conditions and the necessary amount of fabric property correction in fabric Finishing. Describes and discusses these systems and puts forward arguments and new ideas for future research in advance garment manufacture.

Despite the globalization and internalization of competition and surplus of apparel production, high labour costs and other economic pressures, apparel products are still being produced using traditional methods and machinery, the mechanics of which have not fundamentally changed since the seventeenth century, even nowadays when the materials produced are very flexible and diverse in texture and properties. In developing the industry further, the nature of interaction between machinery, fabric and operatives has to be taken into account, and this poses some real problems if one has to put forward realistic solutions for future industrial development. It is therefore important to be able to take into consideration fabric/machine/human interactions during the manufacturing process in order to propose the next generation of manufacturing systems which is much needed in the current apparel industry. Reports on findings in the area of intelligent garment manufacture which is a means of introducing flexibility, quality, production efficiency and maximization of resources to the apparel industry. Primarily emphasizes the importance of fabric properties and their interaction with the whole manufacturing process, the labour force and especially with sewing. In order to achieve this, applies computational intelligence and engineering to research, develop and implement intelligent textile and apparel environments, and introduce desired flexibility into the whole area of textile and apparel processes, especially in terms of quick response (QR) and just in time (JIT).

The purpose of this research is to study the effect of softener treatment on plain jersey fabrics with properties made of cotton and spandex yarn.

Samples with 100 percent cotton yarns, spandex yarns in alternating courses (half plating) and spandex yarns in every courses (full plating) were produced on a circular knitting machine where the two latter cases were produced at five different levels of spandex extension. After the dyeing process, fabrics were treated with fabric softener using two softener types (cationic and silicon) and all type two concentrations (3 percent, 6 percent) to evaluate the most appropriate softener type and concentration on fabric friction force, sewing needle penetration force and weight loss percent under different levels of spandex extension.

Results showed that silicon softener treatment results in high decreases in fabric sewing needle penetrating force, friction force and while treatment with cationic softener results in high decreases in weight loss percent for 100 percent cotton, half and full plating fabrics.

There is a growing need to study the effect of softeners when spandex yarns are used in the production of knitted fabric which results in high increase of stitch density. This research compares the effects of two different softener types at different concentrations on the properties of both plain jersey fabric produced from 100 percent cotton yarns and from cotton/spandex yarns with different stitch density.