In order to improve the external quality of clothing, scientific and technological personnel in the clothing industry at home and abroad in recent years have started from the relationship between clothing fabrics, fusible linings and bonding processes and post-bonding quality. How to plan curtains for activities? This article comes from the Online Textile City . Please explain when reprinting! , a series of studies have been conducted on the reasonable compatibility of clothing fabrics and fusible interlinings and the prediction of bonding quality. The researchers have successfully established a model to predict the feel and drape of the bonded composite based on the fabric mechanical properties measured by FAST experiments. The KES fabric evaluation system can then be used to determine the combination of various parts of the garment and the interlining. plan. However, because the impact of the basic structural functions of the interlining and fabric on the bonding quality is ignored, clothing designers cannot decide on the ideal bonding combination plan based on the functions of the fabric and interlining. In view of the characteristics of the selection of surface accessories for professional attire, and in order to adapt to the development trend of high-quality, automated assembly line production of professional attire in my country, this paper is based on domestic and foreign New research here examines ways to predict bonding quality from the basic structure of fabrics and interlinings.
Fusible interlining parameters: thickness of base fabric, surface fullness of interlining fabric, resin particle size, coating amount, yarn twist coefficient of interlining fabric, etc.;
Stop words
Increase the richness of clothing
Workwear Exploration of new methods for predicting bonding quality
Functional parameters of the composite after bonding the adhesive lining to the fabric: peeling Strength, dry cleaning dimensional changes, water washing dimensional changes, washing resistance (including water washing and dry cleaning), glue penetration area, drape, analytical hand value THV, free formaldehyde content, etc.
Wild neural network method
The collar, pockets, cuffs, belts and other parts of business attire need to maintain a stable shape and certain stiffness. This point is even more prominent for clothing made of thin fabrics. Due to the use of lining, the garment has an extra layer of protection, so that the fabric will not be deformed due to excessive stretching, making the garment more washable and durable.
In the past two decades, interlining bonding technology has made great progress. As the “skeleton” and “backbone” of clothing, fusible interlining plays an indispensable and important role in the production and manufacturing of professional clothing. Business wear The use of fusible interlining mainly depends on the shape of the tailor and the characteristics of the clothing fabric. In terms of quality, the adhesive lining should generally achieve the following results:
Knowledge base and machine learning are an artificial intelligence method in which computers simulate human thinking processes to automatically acquire knowledge and are used to solve complex problems. In terms of engineering practice, the problems of artificial intelligence management are becoming increasingly complex. Through machine learning, databases and information systems are automatically compressed into knowledge libraries, which can automatically solve complex problems. Scientific and technical personnel in the clothing industry have conducted research and theoretical analysis and used the knowledge base method to better analyze and predict the forming properties of the bonded composite.
Common parameters that affect the bonding quality of Professional Wear fabrics and interlinings
Fabric parameters: fabric density, structure, fabric fullness, thickness, fabric yarn thread Density and twist coefficient, etc.;
The bonding quality of fabrics and interlinings depends on many factors such as fabrics, interlinings and pressing conditions. Decoration researchers performed variance analysis on the structural properties of a large number of fabrics and interlinings. The variance The results of the analysis result show that the structural and functional combination of the fabric and interlining and the bonding conditions are important factors that affect the quality of the bonded composite article. The structural and functional parameters that have a significant impact are reflected in the scatter plot. The dot plot shows that the quality grade of the bonded composite increases with the increase of the fabric’s analytical hand value THV, cloth fullness and thickness, but decreases with the increase of the fabric yarn twist coefficient; with the increase of the fabric’s analytical hand value THV , increases with the increase of cloth surface fullness and thickness, but increases with the increase of resin amount, resin particle size and interlining yarn twist coefficient. Due to the interdependence between pressing temperature, time and pressure, it is not possible to discern the respective effects of bonding time, temperature and pressure on the quality grade of the bonded composite. Due to the complex interactions among various factors that affect bonding quality, it is difficult to make further detailed predictions on bonding quality using traditional methods. Because artificial neural networks have the powerful ability to predict the fault tolerance and adaptability of nonlinear structural systems, Taiwanese clothing researchers adopted a three-layer BP network structure. The three layers are divided into input layer, output layer and hidden layer. The 13 structural and functional parameters of the fabric and interlining are used as the input values of the network, and the quality grade of the bonded composite is used as the input value of the network. The quality grade of the bonded compound was successfully evaluated based on the structural and functional parameters of the fabric and interlining. Looking forward. The residual analysis results of artificial neural networks show that artificial neural network models can indeed and effectively predict the final quality grade of adhesives based on the structural functions of fabrics and interlinings.
Knowledge base methods
Business attire represents corporate and personal image, so Business attire must meet the requirements of good appearance , and must be able to fully meet the needs of task activities. The use of adhesive lining greatly enhances the function of business attire in terms of shape retention and strength. With the development of business attire and the continuous increase in the types of business attire fabrics, it has become a secondary issue to continuously improve the water-temperature performance of business attire lining fabrics. necessary subject. CareerBy demonstrating knowledge to predict the forming properties of bonded composites and analyzing the interrelationships between specific parameters, we successfully predicted the forming properties of wool bonded composites for tops based on the structural properties of the fabric. At the same time, the research shows that adding more learning samples to the database in machine learning can make the knowledge base adapt to the analysis and prediction of the bonding quality of more types of surface accessories. Research and application work in this field will also show a broader scope. space.
In the construction of business attire, in order to obtain the ideal appearance of the clothing, the types and quality of fabrics and fusible linings must be coordinated and consistent, and the influence of the composite after bonding must be understood. The interaction between parameters of physical properties requires a large amount of professional knowledge and data. Machine learning sample generation knowledge base technology can learn the facts and relationships required in any specific field, simplifying the process and proposing an analysis of the functional relationships and predictions between business wear fabrics and fusible linings. A new approach to the function of composites after bonding.
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