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Application of Artificial Intelligence in Modeling a Textile Finishing Process

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Reliability and Statistical Computing

Part of the book series: Springer Series in Reliability Engineering ((RELIABILITY))

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Abstract

Textile products with faded effect are increasingly popular nowadays. Ozonation is a promising finishing process treatment for obtaining such effect in the textile industry. The interdependent effect of the factors in this process on the products’ quality is not clearly known and barely studied. To address this issue, the attempt of modeling this textile finishing process by the application of several artificial intelligent techniques is conducted. The complex factors and effects of color fading ozonation on dyed textile are investigated in this study through process modeling the inputs of pH, temperature, water pick-up, time (of process) and original color (of textile) with the outputs of color performance (\(K/S, L^*, a^*, b^*\) values) of treated samples. Artificial Intelligence techniques included ELM, SVR and RF were used respectively. The results revealed that RF and SVR perform better than ELM in stably predicting a certain single output. Although both RF and SVR showed their potential applicability, SVR is more recommended in this study due to its balancer predicting performance and less training time cost.

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Abbreviations

ELM:

Extreme Learning Machine

SVR:

Support Vector Regression

SVM:

Support vector Machine

RF:

Random Forest

ANN:

Artificial Neural Network

SLFNs:

Single-Layer-Feedforward-Neural-Networks

RB-RN:

Reactive blue FL-RN

RR-2BL:

Reactive red FL-2BL

RY-2RN:

Reactive yellow FL-2RN

MAE:

Mean absolute error

RMSE:

Root mean square error

R:

Correlation coefficient

MRAE:

Mean relative absolute error

LOO:

Leave one out

MRF:

Multivariate Random Forest

W:

Input weight matrix

\(\beta \) :

Output weights

\(\varepsilon \) :

Threshold

\(\xi _i\) :

Slack variables, upper constraints on the outputs of the system

\(\xi _i^*\) :

Slack variables, lower constraints on the outputs of the system

L :

Lagrangian

\(\eta _i, \eta _i^*, \alpha _i^*, \alpha _i^*\) :

Lagrange multipliers

\(\gamma , \lambda \) :

Positive regularized parameters controlling the bias-variance trade-off in SVM

p :

Parameter of RBF that sets the spread of the kernel

ntree :

The number of trees in the forest

minleaf :

Minimum number of samples in the leaf node

mtry :

Randomly selected features considered for a split in each regression tree node

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Author information

Authors and Affiliations

  1. GEMTEX – Laboratoire de Génie et Matériaux Textiles, ENSAIT, 59000, Lille, France

    Zhenglei He & Sébastien Thomassey

  2. ENSAIT & GEMTEX, Roubaix, France

    Kim Phuc Tran & Xianyi Zeng

  3. National Local Joint Engineering Laboratory for Advanced Textile Processing and Clean Production, Wuhan Textile University, Wuhan, 430200, China

    Changhai Yi

Authors
  1. Zhenglei He
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  2. Kim Phuc Tran
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  3. Sébastien Thomassey
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  4. Xianyi Zeng
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  5. Changhai Yi
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Corresponding author

Correspondence to Zhenglei He .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ, USA

    Hoang Pham

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He, Z., Tran, K.P., Thomassey, S., Zeng, X., Yi, C. (2020). Application of Artificial Intelligence in Modeling a Textile Finishing Process. In: Pham, H. (eds) Reliability and Statistical Computing. Springer Series in Reliability Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-43412-0_5

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  • DOI: https://doi.org/10.1007/978-3-030-43412-0_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43411-3

  • Online ISBN: 978-3-030-43412-0

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