Machine learning-based marker length estimation for garment mass customization

Xu, Y. N.; Thomassey, Sebastien; Zeng, Xianyi

Document type :

Article dans une revue scientifique: Article original

DOI :

10.1007/s00170-021-06833-w

Permalink :

http://hdl.handle.net/20.500.12210/84908

Title :

Machine learning-based marker length estimation for garment mass customization

Author(s) :

Xu, Y. N. [Auteur]
Zhejiang Sci-Tech University
Thomassey, Sebastien [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
Zeng, Xianyi [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]

Journal title :

The International Journal of Advanced Manufacturing Technology

Abbreviated title :

Int. J. Adv. Manuf. Technol.

Volume number :

Pages :

Publication date :

2021-04-01

ISSN :

0268-3768

English keyword(s) :

Marker making
Multiple linear regression
Neural network
Mass customization

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

The quick development of mass customization in the apparel industry leads to an exponential increase of garment size combinations for markers, which induces a heavy and complex workload of marker making. In this context, ...
Show more >The quick development of mass customization in the apparel industry leads to an exponential increase of garment size combinations for markers, which induces a heavy and complex workload of marker making. In this context, due to the complexity of the problem, the classical marker making methods using the existing commercialized software are less performant in terms of efficiency and accuracy. Therefore, machine learning techniques, usually taken as efficient tools for extracting relevant information from data measured in uncertain and complex scenarios, are considered much simpler and faster. In this study, we apply the methods of multiple linear regression (MLR) and radial basis function neural network (RBF NN) to estimate marker lengths that are used in various garment production modes by considering various sets of garment sizes and different marker types. The experimental results show that the proposed approach leads to a good performance in estimating marker lengths of different types of markers (mixed marker and group marker) with diverse size combinations taken from various sets of garment sizes in both mass production and mass customization conditions.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
ENSAIT
Junia HEI

Collections :

Génie des matériaux textiles (GEMTEX) - ULR 2641

Research team(s) :

Human-Centered Design

Submission date :

2023-06-20T11:43:29Z
2024-03-13T07:35:20Z
2024-03-13T07:36:50Z

Version	Link	Modification date
3	20.500.12210/84908*	2024-03-13T07:36:07Z
2	20.500.12210/84908.2	2024-03-13T07:26:32Z
1	20.500.12210/84908.1	2023-06-20T11:43:29Z

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Machine learning-based marker length ...

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