Review of Artificial Intelligence Applications in Garment Manufacturing

Jelil, Radhia Abd

doi:10.1007/978-981-13-0080-6_6

Cited by 15 publications

(6 citation statements)

References 70 publications

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“…Together, the Internet of Things and cyberphysical systems integrate the physical and virtual worlds, using devices such as sensors, actuators, mobile devices, and smart gateways for interoperability, transparency, decentralized decision-making, real-time communication, and self-optimization (Dal Forno et al, 2021;Park et al, 2020). Further, technology such as artificial intelligence, simulation, machine learning, and robotics are adopted in production order scheduling, marker making, line balancing, machine layout designing, detecting defects, and inspecting comfort of garments (Abd Jelil, 2018). Techniques such as neural networks, genetic algorithms, and fuzzy logic enable better reasoning, learning, problem-solving, decisionmaking, and communication to improve efficiency and productivity in manufacturing (Noor et al, 2021).…”

Section: Industry 40 Technologies For Smart and Sustainable Manufactu...mentioning

confidence: 99%

“…Specifically, virtual reality and additive manufacturing help to reduce the resources consumed in creating multiple physical samples (Papahristou & Bilalis, 2017). Industry 4.0 technologies, including artificial intelligence, machine learning, automation, cloud computing, and the Internet of Things, digitalize manufacturing activities, in turn assisting in reducing lead time and improving agility and flexibility (Abd Jelil, 2018;Dal Forno et al, 2021). Further, there is evidence that artificial intelligence can improve productivity in quality assurance by improving defect detection, overcoming human errors in quality inspection, and reducing product recalls (Noor et al, 2021).…”

Section: The Promise For Economic Sustainabilitymentioning

confidence: 99%

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The Promise of Industry 4.0 Technologies for the Sustainability of Developing Country Apparel Manufacturers

Gunaratne,

Sinkovics,

Sinkovics

2024

SSRN Journal

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Section: Industry 40 Technologies For Smart and Sustainable Manufactu...mentioning

confidence: 99%

Section: The Promise For Economic Sustainabilitymentioning

confidence: 99%

The Promise of Industry 4.0 Technologies for the Sustainability of Developing Country Apparel Manufacturers

Gunaratne,

Sinkovics,

Sinkovics

2024

SSRN Journal

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“…These elements play a vital role in increasing performance and productivity. There are many academic studies, especially simulation [20], quality control [21][22], image perception [22], data mining [23,24], decision making in apparel manufacturing [25][26][27][28], advancement of textile manufacturing processes [29], production and demand forecasting in textile industry [30][31]. In the literature review, the use of data mining in the textile industry and the studies on production defects were examined.…”

Section: Literature Reviewmentioning

confidence: 99%

Fabric and Production Defect Detection in the Apparel Industry Using Data Mining Algorithms

Ersöz

ZAHOOR

Ersöz

2021

International Journal of 3D Printing Technologies and Digital Industry

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Nowadays, technology plays a crucial role in fabric production in the textile industry. The demand for high-quality products and rapidly changing economic conditions increase the significance of readymade clothing manufacturers to produce the right quality product. In addition, in order to minimize production errors, to improve and maintain process performance, it is important to identify the sources of variability during manufacturing. The defective fabric is the main reason which is causing harm to the textile business. Therefore, the proper identification of manufacturing defects leads to a successful business. When it comes to extracting meaningful insights from data and knowledge discovery, data mining has proven its significance in various fields such as business, health, finance, and education. As in all other sectors, data mining is widely used in the textile sector too. In this study, it was aimed to determine the main causes of the error with defective production data of a company that produces clothing by using data mining methods. Decision Tree, Naive Bayes, Random Forest, and Gradient Boosted Trees Algorithms were used in the research. Accuracy rate and Cohen's kappa statistics were taken for comparison algorithms in the study. While determining the main reasons for defective products, the factors of which type of products the company produces for its customers, the sizes of the defective products, types of defects, and explanations were taken into consideration. The most common mistakes in sewing production and the main source of the error were evaluated. According to the results, suggestions were made for the company to take various measures.

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“…However, the system does not use interactive fitness functions. More details about the latest artificial intelligence and machine learning-based systems for garment recommendation is available in (Abd Jelil, 2018). Ensemble machine learning is used in the work of (Lukyamyzi, Ngubiri, & Okori, 2020) in the food industry domain.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Computational Intelligence System for Fashion Design

Sajja

2021

International Journal of System Dynamics Applications

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The domain of fashion design evolves continuously and is highly personalised, demanding intelligent and customised recommendation. The traditional artificial intelligence-based systems offer solutions based on stored knowledge; hence they can be quickly obsolete and require high effort. To meet the fashion designers’ needs and provide tailor-made recommendations effectively, a hybrid genetic-fuzzy system is proposed with interactive fitness functions. The system is based on generic hybrid architecture using fuzzy logic and genetic algorithm, which can be used to evolve various products in different domains and tested with interactive fuzzy fitness functions. The design of the generic architecture meets the research gap identified through an in-depth literature survey. To prove the utility of the architecture, an experiment is carried out showing encoding scheme, genetic operators, fuzzy membership functions, and fuzzy rules. The results are also discussed, along with the comparison, advantages, applications, and possible future enhancements.

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Review of Artificial Intelligence Applications in Garment Manufacturing

Cited by 15 publications

References 70 publications

The Promise of Industry 4.0 Technologies for the Sustainability of Developing Country Apparel Manufacturers

The Promise of Industry 4.0 Technologies for the Sustainability of Developing Country Apparel Manufacturers

Fabric and Production Defect Detection in the Apparel Industry Using Data Mining Algorithms

Hybrid Computational Intelligence System for Fashion Design

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