Design of the system for prediction of fabric behaviour in garment manufacturing processes

Pavlinić, Daniela Zavec; Geršak, Jelka

doi:10.1108/09556220410520522

Cited by 10 publications

(6 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, a machine learning method for the prediction of a fabric behaviour refers to a wide range of fabric behaviour studies. On the basis of the input data (parameters of mechanical properties) and input knowledge (fabric behaviour responses) it offers the prediction of fabric behaviour in garment manufacturing processes [8].…”

Section: Introductionmentioning

confidence: 99%

Relationship between mechanical properties of bilayer textile systems and their components

Ancutienė¹,

Strazdiene²,

Papreckiene³

2012

Proc. Estonian Acad. Sci.

View full text Add to dashboard Cite

The goal of this research was to defme 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 gW), 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 deñned. The obtained theoretical relationships were confirmed experimentally by fmding 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 fmal textile product.

show abstract

Section: Introductionmentioning

confidence: 99%

Relationship between mechanical properties of bilayer textile systems and their components

Ancutienė¹,

Strazdiene²,

Papreckiene³

2012

Proc. Estonian Acad. Sci.

View full text Add to dashboard Cite

show abstract

“…The next stage is yarn production jus after fibre manufacturing. There are certain factors to evaluate the sustainability level of fibre spinning technologies such as speeds of working machines, fibre type, various spindle lines, thickness of yarn, twist level [43]. Technologically, this system opens the cotton bales & eliminates the impurities, then stretches & twists the fibres, gathers the yarn generated and distributes it to blow room to separate them to transform it into yarn [44].…”

Section: Bhybrid Sustainable Practices In Yarn Productionmentioning

confidence: 99%

Textile Industry 4.0 Transformation for Sustainable Development: Prediction in Manufacturing & Proposed Hybrid Sustainable Practices

Rathore

2022

EIPRMJ

View full text Add to dashboard Cite

In today’s scenario textile industry is playing the biggest role in the world’s main sources of industry pollution and making the environment issues main concern across the globe. The innovation of technology has paved the path for textile industry transformation with the help of industry 4.0. This research article proposes the combination of proposed hybrid sustainable practices and textile industry 4.0 transformation strategies for green & smart sustainable future. The PHSP’s has three characteristics as follows: 1. Helps to resolve pollution to attain sustainable development, 2.Sustainability in terms of safety & resources of environment, and 3.Prediction in manufacturing before decision making. By taking the motivation from UNs’Goals of Sustainable Future Development & Importance of Sustainability, this research investigates, the textile industry 4.0 can comply with hybrid sustainable practices. To analyse the hybrid sustainable practices towards sustainable future, the enactment of Proposed Hybrid Sustainable Practices was integrated with Intelligent MESN (Modified Echo state Network) Model. We figured out that the current operations of sustainability and production in Textile/Garment industry can be predicted before decision making & feedback can be obtained at the same moment. It will help the textile/garment industry in the prediction of pre problems & we can resolve them before the problem occur whether it is related to sustainability or environmental pollution. For instance, the purpose of “Accountable & Sustainable Production & consumption”, “Hygiene & Clean Water to help Environment”, and “Action towards the Climate” can be resolved before the occurrence by Proposed Hybrid Sustainable Practices

show abstract

“…Machine learning has been implemented in the F&A industry for sales prediction [38], trend analysis, color prediction [39], demand forecasting [40], fabric defect detection [41], predicting fabric behavior using mechanical properties [42].…”

Section: ) Machine Learningmentioning

confidence: 99%

A Detailed Review of Artificial Intelligence Applied in the Fashion and Apparel Industry

et al. 2019

View full text Add to dashboard Cite

The enormous impact of artificial intelligence has been realized in transforming the fashion and apparel industry in the past decades. However, the research in this domain is scattered and mainly focuses on one of the stages of the supply chain. Due to this, it is difficult to comprehend the work conducted in the distinct domain of the fashion and apparel industry. Therefore, this paper aims to study the impact and the significance of artificial intelligence in the fashion and apparel industry in the last decades throughout the supply chain. Following this objective, we performed a systematic literature review of research articles (journal and conference) associated with artificial intelligence in the fashion and apparel industry. Articles were retrieved from two popular databases ''Scopus'' and ''Web of Science'' and the article screening was completed in five phases resulting in 149 articles. This was followed by article categorization which was grounded on the proposed taxonomy and was completed in two steps. First, the research articles were categorized according to the artificial intelligence methods applied such as machine learning, expert systems, decision support system, optimization, and image recognition and computer vision. Second, the articles were categorized based on supply chain stages targeted such as design, fabric production, apparel production, and distribution. In addition, the supply chain stages were further classified based on business-to-business (B2B) and business-to-consumer (B2C) to give a broader outlook of the industry. As a result of the categorizations, research gaps were identified in the applications of AI techniques, at the supply chain stages and from a business (B2B/B2C) perspective. Based on these gaps, the future prospects of the AI in this domain are discussed. These can benefit the researchers in academics and industrial practitioners working in the domain of the fashion and apparel industry.INDEX TERMS Artificial intelligence, big data analytics, machine learning, expert systems, fashion and apparel industry.

show abstract

Design of the system for prediction of fabric behaviour in garment manufacturing processes

Cited by 10 publications

References 3 publications

Relationship between mechanical properties of bilayer textile systems and their components

Relationship between mechanical properties of bilayer textile systems and their components

Textile Industry 4.0 Transformation for Sustainable Development: Prediction in Manufacturing & Proposed Hybrid Sustainable Practices

A Detailed Review of Artificial Intelligence Applied in the Fashion and Apparel Industry

Contact Info

Product

Resources

About