Garment Manufacturing Process Research Articles

Fibre fragment pollution: source directed intervention through design

This study analyses the difference in fibre fragment pollution generated during the laundering of woven and knit fabrics for clothing and the influence of fabric parameters on the amount of pollution released. 100% polyester single jersey knit and 2/2 twill woven fabrics were created and washed according to AATCC TM212-2021. Results indicated more tightly woven structures released over three times less pollution than looser knit structures, releasing 6.41 mg of fibre fragments per kg of woven fabric (mean ± 1.50 SD, n = 8) compared to 21.21 mg of fibre fragments per kg of knit fabric (mean ± 1.80 SD, n = 8). The first wash for both knit and woven fabrics shed significantly more fibre fragments than subsequent washes. By the fifth wash both fabrics released under a tenth of the amount of pollution released in the first wash. To mitigate fibre fragment pollution that is released by fabrics designed for clothing this work recommends designing out pollution using more tightly constructed fabrics. Furthermore, the implementation of technology in fabric and garment manufacturing processes (e.g. fibre catching devices) should be utilised specifically during first washes allowing pollution to be filtered from the wastewater to stem the flow of contaminants into the environment.

Flexible Design of Garment Styles to Support the Minimal Waste Concept in the Fashion Industry

Abstract Currently, the design of garment styles is weakly supporting the MWD concept. It is kept fixed during all its very changeable manufacturing process, thereby increasing pre- and post-consumer fabric wastes. To improve the situation the final values of construction parameters which influence fabric use efficiency should be determined for every unique production order only in the garment manufacturing process. Four garment styles were tested to see how light changes in their width and length influence fabric use efficiency. Even minimal reduction of these parameters can give noticeable fabric savings. Different values of these parameters create slightly different shapes of pattern pieces, and with it, give a huge number of new possibilities to create more efficient production markers. The authors found a way how to make the garment designing process more efficient, as well as, to create a “virtual bridge” between garment designing and the manufacturing phase. Both improvements could give serious benefits: reduced fabric use and product price, as well as, reduced pre- and post-consumer textile material wastes.

Simulation analysis and optimization of garment manufacturing process based on Em-Plant software

In recent years, the way of competition has changed in terms of garment manufacturing. Quality and scale competition gradually turn into speed competition. Garment manufacturers need to shorten the production cycle to win development opportunities. This paper takes a garment manufacturing factory as the research object, and innovatively uses the MOST method and Em-Plant simulation to improve the efficiency of its production line. According to each activity, operation time and worker assignment of the garment manufacturing process, the corresponding simulation model based on Em-Plant software is created to reveal the current problems existing in the manufacturing process. By analyzing these results, the bottlenecks in the process are found out. Applying theoretical knowledge such as production line balancing, lean production, motion study and time analysis, the improvement two plans are put forward, and the corresponding simulation results are given subsequently. Finally, a relatively better improvement plan is determined, which can better achieve the goal of balancing production line, improving efficiency, and reducing cost.

Technology and Education Inputs for Productivity Enhancement in Garment Manufacturing in the Developing World

Efficient production management in the garment manufacturing industry is estimated by using work measurement techniques. These techniques are based on standard processing timings. Several studies have been undertaken using the time study technique to enhance productivity of the garment production lines in an apparel manufacturing factory. Efficiency is translated into the time spent on carrying out a particular function, this time consumption is dependent upon the worker’s level of fatigue and quality of output. The entire onus of responsibility of the worker’s output becomes anchored on his or her ability to carry out repetitive actions over long durations of time.Objectives: This paper examines the work of various scholars as well as scrutinizes the performance of certain repetitive actions in the garment manufacturing process in apparel industries employing large numbers of workers. The objective is to project a rationale for the development of work aids to significantly enhance productivity.Methods: A secondary data review is being conducted to find out the work of other researchers in the field of garment manufacturing. The author has developed his own contribution in the form of work aid interventions to help in the process. A Review of literature on the subject was attempted to obtain the required information and insights on the subject.Finding: There is a widely felt need for the creation of active work aids in the garment manufacturing industry to promote the worker efficiency as also the overall productivity of the industry.Scope: The research based study has large and far-reaching scope for the garment manufacturing industry. All manufacturing units are constantly on the lookout for promoting efficiency and productivity. They are willing to appreciate all forms of interventions to improve time management and enhanced output of products. The findings of this review paper have broad based applicability for the garment manufacturing industry not only in India but also in most of the developing world.

Prediction of Garment Production Cycle Time Based on a Neural Network

The process of garment production has always been a black box. The production time of different clothing is different and has great changes, thus managers cannot make a production plan accurately. With the world entering the era of industry 4.0 and the accumulation of big data, machine learning can provide services for the garment manufacturing industry. The production cycle time is the key to control the production process. In order to predict the production cycle time more accurately and master the production process in the garment manufacturing process, a neural network model of production cycle time prediction is established in this paper. Using a trained neural network to predict the production cycle time, the overall error of 6 groups is within 5%, and that of 3 groups is between 5% and 10%. Therefore, this neural network can be used to predict the future production cycle time and predict the overall production time of clothing.

Potentialities of Reducing Textile Waste in the Manufacturing of Garments from Striped Fabrics

Certain material losses are still tolerated in traditional garment manufacturing process. Only 80% of textile materials purchased by industry companies are used for their main purpose. The other 20% lost in garment cutting process are landfilled, incinerated, reused or recycled. Further technical progress is not able to minimise fabric waste significantly. Possibilities to reduce fabric waste have to be found in the garment designing and development process. Styles from cross directionally striped fabrics have raised material losses because of their specific design and fabric pattern. The 1st experiment with a striped T-shirt showed that fabric waste could be critical due to disconformities of the style design with the fabric pattern. The 2nd experiment showed that it is possible to reduce fabric waste by conforming the length of the style with the fabric pattern. Length tolerance – acceptable slight variations in the length of the style which does not change the design and visual perception of the garment style should be determined by designers and used in industry processing of manufacturing orders. The authors of the article developed methodology to perform the work process in an automated way. Certain manipulations of the design of a garment style directly in its manufacturing process have never been done before, however they could be very effective in producing medium and large manufacturing orders as they would help to reduce post-industrial fabric waste, material consumption, as well as the product cost.

A Systematic Review on Smart Manufacturing in the Garment Industry

Since Industry 4.0, there is a growing interest in smart manufacturing across all industries. However, there are few studies on this topic in the garment industry despite the growing interest in implementing smart manufacturing. This paper presents the feasibility and essential considerations for implementing smart manufacturing in the garment industry. A systematic review analysis was conducted. Studies on garment manufacturing and smart manufacturing were searched separately in the Scopus database. Key technologies for each manufacturing were derived by keyword analysis. Studies on key technologies in each manufacturing were selected; in addition, bibliographic analysis and cluster analysis were conducted to understand the progress of technological development in the garment industry. In garment man ufacturing, technology studies are rare as well as locally biased. In addition, there are technological gaps compared to other manufacturing. However, smart manufacturing studies are still in their infancy and the direction of garment man ufacturing studies are toward smart manufacturing. More studies are needed to apply the key technologies of smart man ufacturing to garment manufacturing. In this case, the progress of technology development, the difference in the industrial environment, and the level of implementation should be considered. Human components should be integrated into smart manufacturing systems in a labor-intensive garment manufacturing process.

Garment mass customization methods for the cutting-related processes

In the garment industry, upgrading from mass production to mass customization is imperative, due to ever-changing fashion trends and customers’ increasing personal demands. Based on pattern variations, several practical mass customization methods in terms of custom-fit and co-design are developed in this study for cutting-related processes (i.e. the sizing process and the cutting process). The increment of size number by generating additional sizes and the expansion of size capacity by setting multi-sized darts are two custom-fit methods, while material (fabric) variation by spreading “rainbow plies” and module variation by making a stepwise cutting are two co-design methods. Compared with existing mass customization methods mainly related to the garment design processes, the proposed methods emphasize the garment manufacturing processes in order to resolve the conflict between personalization and cost. A case study on the women’s basic straight skirt is conducted for the performance evaluation (about personalization and cost) of the proposed methods. The experimental results demonstrate the ability of these methods to significantly raise the personalization level with an acceptable price in garment customization. This study provides a reference for garment manufacturers to make proper production strategies, enabling garment mass customization.

System of error detection in the manufacture of garments using artificial vision

A computer vision system is implemented to detect errors in the cutting stage within the manufacturing process of garments in the textile industry. It provides solution to errors within the process that cannot be easily detected by any employee, in addition to significantly increase the speed of quality review. In the textile industry as in many others, quality control is required in manufactured products and this has been carried out manually by means of visual inspection by employees over the years. For this reason, the objective of this project is to design a quality control system using computer vision to identify errors in the cutting stage within the garment manufacturing process to increase the productivity of textile processes by reducing costs.

Evolving training modules for hearing impaired individuals for gainful employment in garment manufacturing processes

Abstract The present study was undertaken to create job opportunities for hearing-impaired individuals in garment-manufacturing and sequin-finishing units. Employment is not only a statistical and practical issue but also a human rights and entitlement issue. The differently abled are no exception to this basic right. Disability is no longer a welfare or charity issue. However, unlike non-disabled people, people with disabilities, more often than not, face difficulties in getting educated and in accessing vocational training and higher education. Working with people with disabilities can be challenging, but by providing them with work and recognition, it can be the beginning of another way of life rather than the end of life for them. It is important to identify their capabilities and accordingly find suitable jobs, wherein with proper training along with encouragement, compassion, and constant motivation, they can effectively adapt to and settle in specific job operations. This study identifies various operations in different sections of these units and explores the training possibilities there, in an attempt to make the hearing impaired employable. Because of their limited abilities, it requires additional efforts for them to compete in the open labor market. Thus, this is an attempt to prove that hearing-impaired individuals can be gainfully employed in open employment situations.

FabricVision: System of Error Detection in the Manufacture of Garments

A computer vision system is implemented to detect errors in the cutting stage within the manufacturing process of garments in the textile industry. It provides solution to errors within the process that cannot be easily detected by any employee, in addition to significantly increase the speed of quality review. In the textile industry as in many others, quality control is required in manufactured products and this has been carried out manually by means of visual inspection by employees over the years. For this reason, the objective of this project is to design a quality control system using computer vision to identify errors in the cutting stage within the garment manufacturing process to increase the productivity of textile processes by reducing costs.

A Time Based Correlated Logical Methodology for Choosing Optimum Lean Tools and Techniques in Garment Manufacturing Scenario

Lean tools and techniques have been emerged to eliminate manufacturing waste and thus increase operational efficiency in garment manufacturing process. Implementing lean tools and techniques requires a huge amount of resources and in practice; manufacturers meet difficulties to choose optimum lean tools and techniques within their resource constraints and to find the perceived value of manufacturing waste elimination in garment manufacturing process. There is currently no logical methodology is available for selecting suitable lean tools and techniques within a manufacturer‘s resource constraints. This research goal is to develop time based mathematical models and a logical methodology for choosing optimum lean tools and techniques. Mathematical models were formulated and a logical methodology was developed for calculating the perceived value of lean tools and techniques to manufacturing waste elimination and a stage-by-stage methodology is provided for choosing optimum lean tools and techniques to maximize the manufacturing performance within their limited amount of available resources constraints to eliminate their determined wastes. A computer programme has been developed in MATLAB to solve the developed equations to attain optimum result. Developed MATLAB programme is achieved to find optimum lean tools and techniques for identified wastes within the manufacturer resources constraints using the methodology and mathematical model. A waste relations matrix (WCM) has been proposed to establish the relations between the manufacturing wastes and lean tools and techniques. Using waste relations matrix, utilizing the proposed methodology and developed mathematical model for maximize perceived value of elimination of a manufacturer‘s wastes by implementing suitable lean tools and techniques within their resources constraints. Results also establish that the perceived value of elimination of manufacturing wastes can meaningfully be altered based on manufacturer polices. The planned methodology can also be used dynamically by altering the input in the programme developed in MATLAB. Thus by finding suitable lean tools and techniques for particular garment manufacturing wastes, a manufacturer can better prioritize implementation efforts and resources to maximize the success of implementing lean tools and techniques in their garment industries.

A Cost Based Correlated Logical Methodology for Choosing Optimum Lean Tools and Techniques based on Lean Survey in Garment Manufacturing Industries

The lean is becoming one of the widely recognized continuous improvement tools available to various garments manufacturing industries. All industrialist manufacturers meet difficulties to choose optimum lean tools and techniques within their resource constraints and to find the perceived value of manufacturing waste elimination in garment manufacturing process and will be benefited more by adopting the idea of this research work. lean survey from garment industries results stating that four types of waste which seriously affects garment firms. Mathematical models and waste relations matrix were formulated and logical methodology was developed for calculating the perceived value of lean tools and techniques to manufacturing waste elimination and a stage-by-stage methodology is provided for choosing optimum lean tools and techniques to maximize the manufacturing performance within their limited amount of available resources (cost) constraints to eliminate their determined wastes by relative ranks allotted in waste relation matrix based on ranking of lean survey as a inputs in the C programme.

Konfeksiyon İşlemleri ile Kumaş Özellikleri Arasındaki İlişkinin İncelenmesi

The evaluation of clothing appearance is critical to product development and quality assurance in clothing industry. Handle of fabric and making-up performance (tailorability) are interrelated and represent key quality parameters for clothing manufacturers and consumers. Clothing manufacturers require that the fabric is easy to tailor, passes through the garment manufacturing process easily and that the finished garment has a good appearance. The production of garments from high quality fabrics not only gives comfort to the wearer but also helps in the smooth working of manufacturing processes and leads to almost defect-free garments. In this study, we have investigated the relationship between fabric properties and clothing processes and we have tried to find “ideal” fabric for good handle and good garment appearance

Formability analysis of worsted woven fabrics considering fabric direction

Over years predicting fabric behaviour during garment manufacturing process was considered by researchers in order to reduce manufacturing problems and achieve high quality products. Fabric formability which is affected by bending and tensile behaviour through the application of small loads is a property which can predict fabric performance precisely. However, this property changes regarding fabric direction and is not constant. In this study, fabric formability is examined for worsted woven fabrics in a more detailed way by evaluating this property in various fabric directions. It was concluded that fabric formability could be expressed as a sinusoidal function of sample orientation towards warp direction. Moreover, studying several weave structures with different weft densities reveals that the less the firmness of fabric construction the more the value of fabric formability which indicates the better adaption of fabric to the exerted deformation.

Relationship between mechanical properties of bilayer textile systems and their components

The goal of this research was to define the relationship between the mechanical properties of fused bilayer textile systems and their constituent layers, i.e. fabrics and fusible interlinings. The objects of investigation were four samples of broken twill outer fabric different in thickness (1.01-2.28 mm) and mass per square metre (222-398 g/m 2 ), and two types of fusible interlinings: nonvowen with longitudinal threads and warp knitted. Fused bilayer textile systems were created by changing the orientation of the fusible interlining by 0°, 45°, and 90° in respect to the outer fabric's warp direction. Mechanical properties (tensile, shear, bending, and surface properties) of fused textile systems and their components were determined using KES-F automated testing devices. The relationships z = z(x, y) between fused textile systems' mechanical parameters (z) and parameters of the outer fabrics (x), as well as parameters of fusible interlinings (y) in 0°, 45°, and 90° orientations were defined. The obtained theoretical relationships were confirmed experimentally by finding proper mechanical properties of fusible interlinings (y) on the basis of which bilayer textile systems with mechanical properties (z) falling into the allowable value range of the KES-F quality chart could be composed. Selection of compatible components for fused textile systems with certain mechanical parameters allows avoiding problems in garment manufacturing processes and predicting the appearance and quality of the final textile product.

Upper‐Bounding the Incidence Rate of Associations Between Camouflage Uniforms and Surveillance Images*

Camouflage garments can be associated with surveillance images of a crime scene even in the absence of unique wear marks or very high-quality images. However, the probability of an accidental association, or incidence rate, is significant. The present work describes and validates a method for estimating the incidence rate based on a statistical model of the garment manufacturing process. The model was developed primarily for use with the current U.S. Army Combat Uniform (ACU), but can be applied to any camouflage garment. Eight garment manufacturers were studied, and all sources of variation in the manufacturing process were characterized. The marking and spreading procedures were found to be dominant and consistent sources of variation. However, some sources of variation, in particular those because of human operators, were not consistent enough to accurately characterize. Sources of variation that could not be well-characterized were ignored in the statistical model, yielding a worst-case estimate that is an upper-bound to the true incidence rate. The model was evaluated for a variety of cases. Depending on the quality of the surveillance image, the manufacturing parameters, and the local population, incidence rates range from about 3% to negligibly small. The model was validated by returning to one manufacturer, and sampling a large number of completed garments and estimating empirical match probabilities. The empirical probabilities validated the estimates of the worst-case incidence rate and also demonstrated that typical incidence rates are significantly lower.

Preliminary investigations into the impact of an ergonomics intervention for garment workers

The garment manufacturing process involves tasks such as fabric laying, pattern making, cutting, stitching, checking, ironing and packing. A preliminary ergonomics evaluation of the checking section indicated that the workers in this section experienced postural discomfort. A work-station was designed considering factors such as type and nature of activity, time spent on the activity and productivity. The new work-station was a height adjustable table, with an adjustable tilting surface. This table was evaluated and the results indicated a reduction in the postural stress. Keywords: musculo-skeletal disorders, RULA, discomfort mapping, postural stress, work table

Knowledge, Attitude and Practices Related to Occupational Health Problems among Garment Workers in Tamil Nadu, India

The garment manufacturing process is a labour intensive task. The workers in this are employed in three different sections namely cutting, stitching and finishing. As these workers perform repetitive tasks throughout the workday, they face several work related problems. The aim of this study was to assess the level of awareness of health problems among garment workers and their attitudes and practices to prevent the same. In a cross-sectional study (n=216) the workers employed in the three sections had high levels of knowledge of the health problems, but the knowledge of personal protective equipment differed by section. More than one half of the workers in all the sections were aware of the benefits of personal protective equipment (PPE), but only a few workers in the cutting section were using PPE. There was a wide gap between their knowledge level and practice with protective devices.

Predicting Seam Appearance Quality

The appearance of a garment is affected by the quality of the fabrics used in its manufacture, as well as a number of factors determined by the technology of the garment manufacturing process. Since fabric quality, as the most important element of garment appearance, is determined by its mechanical properties, it is obvious that these properties directly impact fabric processing properties. It can be seen through various forms of fabric behavior under the loads that occur in sewing. Investigations of the correlations of the stress and fabric behavior are aimed at constructing a system to predict fabric behavior in garment manufacturing processes, as well as to predict the appearance of the garment to be manufactured. The investigation presented here deals with the impact of fabric mechanical properties on the quality of seam appearance, as defined by seam puckering and work-piece flotation. Machine learning methods included in the Orange software package were used to establish the importance of mechanical properties with respect to fabric behavior.