Garment manufacturing is labor-intensive, which is characterized by low-fixed capital investment; a wide range of product designs and, hence, input materials; variable production volumes; high competitiveness; and often high demand on product quality. However, due to high demand for garment quality and increased consumer awareness is leading to the use of automated tools and equipment in recent years during garment manufacturing. Automation in garment production is becoming a reality due to the technical developments and the use of modeling and simulation. Due to its labor-intensive nature, the apparel industry can seek great benefits out of the AI intervention in their businesses.
In this era of information technology, artificial intelligence (AI) has revolutionized the field of engineering, physics, medicine, and management. Traditional mathematical models are used to solve problems or in the decision-making process, which is the key principle of AI. AI can provide superior solutions to various problems due to its heuristic and intelligent characteristics. Significant results such as improving quality, increasing productivity, and lowering production cost can be achieved with the help of AI. TechKnowGram Limited can assess your need and can propose solutions as needed.
AI systems can provide superior solutions over classical systems due to their heuristic and intelligent nature.
Figure-1: Production processes involved in the apparel manufacturing.
The clothing production process has been described in Fig-1. It starts from the conceptualization phase, passes through design development, manufacturing, supply chain, and retailing till it reaches the consumers. In the conceptualization stage, a designer conceptualizes a theme based on the forecasting of trends in the color, fabrics, silhouette, and trims.
Once the design development has been finalized, the garment production process starts. The garment production process involves fabric spreading, cutting, bundling, sewing, pressing, inspection, and packing (Fig-2).
Figure-2: Process sequence of garment manufacturing.
Fabric is the major component of a garment and it is the input material for many garment-manufacturing industries. Once the fabric is received, the quality is inspected, stored for some time, and then the fabric is spread for cutting. Depending on the garment design, several components are cut using various cutting equipment. The cut components are bundled and fixed with a bundle ticket, which is then passed to the sewing floor.
The garment components are sewn by skilled operators, pressed and the finished garments are inspected for quality. Then the garments are packed and sent to the retailers by own or third-party logistics providers. The retail point is the place where the consumers buy their products. Today’s consumers are much conscious of garment style, fit, quality, and price. The majority of these parameters are governed during the production steps as described in Fig-2. Hence, during these processes, various tasks are needed to be controlled by the managing staff, which may be difficult in several instances. The use of AI can help to control these problems effectively for decision making, cut order planning, marker making, production planning, supply chain management (SCM), and retailing.
Apparel manufacturers have to produce a diverse product mix, as consumers are difficult to understand and predict. Their choice is unstable and unpredictable, and there is a wide variation in their demographics and physiographic. The product quality depends on several factors related to yarn manufacturing, fabric preparation (weaving and knitting), fabric chemical processing, and garment manufacturing. Hence, all these factors can be better controlled by the application of AI in the whole process of apparel manufacturing. Although there is some automation, the apparel industries are still far behind the other sectors and rely on manual intervention.
AI is gaining impetus over the last two decades, in the apparel industry in different areas. The automation of various instruments by the application of AI in spreading, cutting, sewing, and material handling can reduce the production cost and minimize faults.
The production of textiles and clothing involves a large number of variables relating to the material and process. As there is high variability in raw materials in addition to the multiple stages of operation, it is hard to precisely control the process parameters to achieve the desired output. Until now, establishing a proper relationship between these variables and the properties of a fabric depends on human expertise. In many instances, there are chances of error involved with human working, as it is a difficult task to always remember such a large number of variables and apply the knowledge for accurate property prediction. This is possible by the application of AI, as the developments in computation and simulation have created various systems to deal with multiple variables. The application of AI can now deal with a large range of datasets during training to establish an effective relationship between the variables and the product properties. Therefore, over the last decade, the use of AI is rapidly growing in textile and clothing manufacturing industries for various applications.
Applications of Artificial Intelligence in Garment Industry:
AI can be used in various processes of textile products such as fiber grading, prediction of yarn properties, fabric fault analysis, and dye recipe prediction. Similarly, AI can be applied in all the stages (preproduction, production, and postproduction) of garment manufacturing. Garment manufacturing involves processes such as conceptualization, design development, PPC, spreading, cutting, bundling, sewing, pressing, and packaging. Some of the major applications of AI in textile and garment manufacturing are discussed in this section. Out of several types of AI as discussed above, ANN (artificial neural network) is widely used in garment manufacturing mainly in the following fields:
– Prediction of mechanical properties,
– Classification and grading,
– Identification and analysis of faults,
– Process control and online monitoring, and
– SCM and retailing.
The following section describes the application of AI in various production processes involved with garment manufacturing.
Application of artificial intelligence in fiber and yarn production:
Textile fibers are the basic raw material for the production of clothing and other textiles. As there are many different types of textile fibers, it is often difficult to identify an unknown fiber by visual inspection. The traditional practices of fiber identification are based on destructive tests using flame or chemicals.
Recent advancements include the use of optical microscopes, Fourier transforms infrared, and Raman spectroscopy. AI can also be used to identify and grade textile fibers according to their color and other properties such as fineness, length, uniformity ratio, tenacity, and effect of spinning performance on yarn properties. There have been several applications of AI in yarn manufacturing that includes virtual modeling of yarn from fiber properties, prediction of yarn tensile properties, prediction of yarn unevenness, and yarn engineering.
Application of artificial intelligence in fabric production:
The major raw material for the clothing industry is fabric. The quality of the fabric influences the quality of the garment, productivity, and the ease with which garments can be manufactured. The fabrics are selected based on the type of garment and their end-use applications. The fabric specifications for making any garment can be classified as primary and secondary. The physical dimensions are considered to be the primary, whereas the fabric reaction to external forces is considered to be the secondary. From a consumer perspective, garment appearance, comfort, and durability are the important parameters.
AI can be applied to control these parameters:
Predicting fabric properties:
AI can be used to predict the fabric properties before manufacturing with the help of neuro-fuzzy or other approaches by using the fiber, yarn, and fabric constructional data. While applying AI, it is essential to establish a proper linear and nonlinear relationship between the input fiber and yarn parameters and the property of the fabric needs to be predicted. However, the application of AI can be very expensive for the fabric manufacturers, which can increase the cost of production. AI can also be applied to investigate comfort properties. While sensorial comfort is considered, the fabric can be classified according to their hand value by the application of AI.
Color is the first element of design to which the consumers respond, hence it is one of the important features of textiles. Consumers select or reject clothing or other fashion accessories on the basis of color appeal. Hence, for getting the right color, precise quality control during dyeing and printing is essential, which can affect the volume of sales. Both the dyeing and printing process should ensure that the required colorfastness, depth of shade, color matching, and surface characteristics have been achieved. These parameters are influenced by the dye and fabric combinations and the chemical rules governing them. Deviation of these parameters from the allowable limit may lead to reprocessing or rejection of the whole fabric batch.
The use of AI can resolve these issues, which can be used for recipe prediction; process control during dyeing and printing; color matching; and evaluation of the final dyed or printed fabric.
One of the applications of AI for a color solution is during the fiber blending stage, while the roving is converted to yarn. The use of AI can assist in predicting the color produced when fibers of different colors are mixed together. In the case of a homogenous blend, the prediction of color can be performed more accurately by using theoretical and empirical models.
AI can be used for color matching of fabrics and shade sorting. It can be used for true color production by predicting the concentration of dyes from their spectrophotometric absorbance.
Fabric fault detection:
A poor quality fabric can result in substandard garments as well as reduces productivity during garment manufacturing. Any defect in the fabric is passed into the final garment, which can result in the rejection of the garment. Hence, it is essential to check the quality of the fabric before manufacturing the garment.
Generally, fabric inspection is performed by skilled workers using lighted tables or equipment. This process is rather slow and many times can allow faults to pass to the garment. Furthermore, the efficiency of the fabric inspectors will be reduced quickly with fatigue. However, the use of AI can perform this task at a faster rate, with much higher accuracy, and without fatigue.
Figure-3: Different fabric defects inspected (arrow indicates defects) by artificial intelligence: (a) gout, (b) warp float, (c) draw back, (d) hole, (e) dropped stitches, and (f) press-off
Application of artificial intelligence in apparel manufacturing:
The garment manufacturing process is becoming more automated to cater the increasing demand of consumers, reduce the number of faults, and keep the production cost low. AI is increasingly used to predict the performance of a sewn seam, designing of clothing, in PPC, in various sewing operations, and in quality control. AI can be applied for the intelligent manufacturing of clothing to predict the clothing properties after a particular process. So it can be used for suitable garment designing by fabric engineering and monitoring the garment manufacturing processes.
Figure-4: Configuration of a typical automatic inspection system
Performance of sewn seam:
In ABC garments seams and stitches are used to join two or more pieces of fabric together. The ease of seam formation and the performance of the seam are the important parameters, which are judged by the term known as “sewability.” Fabric low-stress mechanical properties such as tensile, shear, bending, and surface can affect sewability. AI system can be used to find the sewability of different fabrics during garment production. Fabric mechanical properties affect the performance during spreading, cutting, and sewing.
A good quality seam is essential for a good quality garment. The performance of a sewn seam depends on the type of fabrics and sewing thread combination; seam and stitch type; and sewing conditions, which includes needle size, stitch density, and the sewing machine condition. The performance properties of the seam are evaluated by seam puckering, seam slippage, and yarn severance, which can be predicted by AI.
Computer-aided design systems:
One of the important steps in garment manufacturing is pattern making, where paper patterns are made by the designers and subsequently digitized to a computer. Several two-dimensional (2D) patterns are prepared for a garment, which are the basic blocks of a 3D garment. Various CAD software are used in the garment industry for pattern making, digitizing, grading, and marker planning. The CAD software helps in achieving high productivity and improved quality. The designers involved in clothing designing create numerous designs by using CAD software.
However, the CAD software cannot be used to automatically generate clothing patterns or designs for a specific garment style. In addition, in many garment industries, the traditional method of garment pattern generation is still done by experienced designers and does not include the use of CAD, although there is the scope of using AI in pattern generation.
Several types of research have been done to implement the AI that can help to develop basic clothing patterns automatically. For example, Inui had developed an AI integrated CAD system (combination apparel CAD and GA) that can be used to search apparel designs that the system users prefer. The search process involves the man-machine interaction cycles, where the user assesses the examples produced by the systems.
CAD systems are used in garment manufacturing for creating designs, patternmaking, and grading operations. Several attempts have been made by researchers to integrate AI with CAD systems to generate designs automatically. Experienced designers are needed for appropriate pattern designs of different clothing styles. However, the AI system can be used to provide the expert knowledge of experienced designers.
Production planning and control:
PPC coordinates between various departments of production so that delivery dates are met and customers` orders are delivered on time. Various research activities focused on the problems related to PPC and avoid bottlenecking. The majority of the studies were based on the problems in PPC relating to the sewing floor such as fixing the machine layout, line balancing in sewing, and managing operators in the sewing floor. AI can be used to solve or optimize the problem of machine layout, operation assignment, and sewing line balancing. This can help in achieving the objectives of PPC.
AI-based decision support system was used in decision making to determine the most appropriate manufacturing plant for a particular customer order. A GA-based real-time segmentation for rescheduling was developed by Wong to deal with the PPC-related problems in sewing floor during (1) marker making, (2) fabric spreading, (3) cutting, and (4) bundling.
Final garment inspection:
The inspection of finished and semi-finished garments during their production is essential to get fewer rejections. The final quality of a finished garment depends on the sewing quality and other faults present in it. The final quality inspection of finished garments is mainly done by experienced people, which is very time-consuming and often subjective in nature. The results of the inspection are influenced by the physical and mental condition of the inspector. Therefore, automated inspection devices are essential to achieve increased efficiency and accurate results. Although limited studies have been done, an automated inspection can be performed by the use of AI and image processing for inspection of the quality of finished garments.
During the garment production, each process (cutting, sewing, and pressing) plays a vital role influencing the quality of the finished garment. The quality of the semi-finished products should be inspected at each of these processes before the final inspection. The finished garments are inspected as per their specifications, overall appearance, faults, and sizing and fit. In detail the finished garments are inspected for stitching quality, mismatched plaids or stripes along the seam, puckered seam or extra material caught in seams, uneven seam along hems, and many other faults that can arise during the garment production.
The application of AI in final garment, inspection includes: automatic classification of general faults in shirt collars (for mono-colored materials) using machine vision; application of AATCC (American Association of Textile Chemists and Colorists) wrinkle rating for evaluation of wrinkle by using a laser sensor; detection and classification of stitching defects using wavelet transform and BP NN; seam pucker evaluation by using self-organizing mapping; and designing of a smart hanger for garment inspection. In manufacturing seamless garments, AI can be used to detect faults online. The image of the final garment can be captured and compared with the standard and any variation from the standard is reported as a fault that can be mended at that time or a marking is done where the fault occurs.
Application in retailing:
Fashion retailing establishes the link between the manufacturers of fashion goods with the consumers. Over the last two decades or so, fashion retailing has become one of the most competitive retail sectors due to technological advancements and behavioral changes of consumers toward fast fashion. There are several areas in retailing such as sales forecasting; fashion retail forecasting; style suggestion to consumers; customer relationship management; demand forecasting; determining customer satisfaction, and fashion coordination; where AI application is ever increasing. Sales forecasting in fashion has become more challenging now due to volatility of demand as it depends on several factors. Historical data on sales in combination with the style, color, and garment size can be used for sales forecasting.
AI suggestion systems can be used for the selection of appropriate styles and design combinations for consumers. In several instances, it is very hard to identify the subtle differences between two different styles. AI can be used to identify the differences and similarities between two or more different styles. Today’s consumers are more aware of the comfort features than before. AI can also be used for the selection of the right type of garment for providing necessary comfort including the appearance, which can be used by the customers.
In this modern era, AI is being used in many areas to solve various problems with intelligence similar to human beings. The application of AI was not widely accepted in labor-intensive clothing production. However, the global competitive environment and a target to achieve low cost of production are the main reasons for the AI’s wider applications in the apparel industry starting from material selection and sourcing, through manufacturing till retailing. AI can be used in various processes of textile products such as fiber grading, prediction of yarn properties, detection of fabric faults, and dye recipe prediction. Similarly, AI can be applied in all the stages of garment production such as preproduction, production, and postproduction operations. Developed countries have already started using AI to improve the quality of garments, enhanced customer service, and hence increased sales. Much progress is undergoing in AI rapidly and in near future it will become an important tool for the garment manufacturers for enhancing quality, increasing production, lowering operating costs, and exercising in house control over overproduction, leading to quick response and just-in-time concept. The application of AI in garment manufacturing has a bright future similar to other areas of application.
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