Modeling color fading ozonation of reactive-dyed cotton using the Extreme Learning Machine, Support Vector Regression and Random Forest

He, Zhenglei; Tran, Kim Phuc; Thomassey, Sébastien; Zeng, Xiaoyang; Xu, Jie; Yi, Changhai

doi:10.1177/0040517519883059

Cited by 19 publications

(16 citation statements)

References 36 publications

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“…According to a previous study [54], a random forest (RF) predictive model is applied to simulate the textile process in this proposed framework and implement the objective functions (3) to earn the agents' rewards. As illustrated in Figure 2 the textile manufacturing process multi-objective optimization problem in the paradigm of MARL, the optimization objectives are abstracted as RL agents, given feedbacks from the RF models integrated with the Markov game environment with state-space formulated in Equation ( 4) that consist of all the parameter variables of the simulated textile process, the agents, are able to evaluate the values of its actions for adjusting the parameter variables with regard to the state (solution) and consequently improve its policy in the environment to optimize objectively gradually.…”

Section: Multi-objective Optimization Of Textile Manufacturing Process As Markov Gamementioning

confidence: 99%

Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning

Tran

Thomassey

et al. 2022

Journal of Manufacturing Systems

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Multi-objective optimization of the textile manufacturing process is an increasing challenge because of the growing complexity involved in the development of the textile industry. The use of intelligent techniques has been often discussed in this domain, although a significant improvement from certain successful applications has been reported, the traditional methods failed to work with high-as well as human intervention. Upon which, this paper proposed a multi-agent reinforcement learning (MARL) framework to transform the optimization process into a stochastic game and introduced the deep Q-networks algorithm to train the multiple agents. A utilitarian selection mechanism was employed in the stochastic game, which ( -greedy policy) in each state to avoid the interruption of multiple equilibria and achieve the correlated equilibrium optimal solutions of the optimizing process. The case study result reflects that the proposed MARL system is possible to achieve the optimal solutions for the textile ozonation process and it performs better than the traditional approaches.

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Section: Multi-objective Optimization Of Textile Manufacturing Process As Markov Gamementioning

confidence: 99%

Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning

Tran

Thomassey

et al. 2022

Journal of Manufacturing Systems

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“…The colour yield of aqueous dyes using ozone has been a popular research topic for decades 10‐14 . Also, for the colour yield of dyed textiles, projects applying ozonation are increasingly reported nowadays 15‐21 . The vintage styles and worn looks of textiles with colour yield effects have gained a growing number of consumers and driven the fashion market dramatically 22,23 .…”

Section: Introductionmentioning

confidence: 99%

A comparative study of ozonation on aqueous reactive dyes and reactive‐dyed cotton

2021

Coloration Technology

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Ozonation is increasingly being used and reported in recent years. This paper investigates ozonation on aqueous reactive dyes and their dyed cotton comparatively. The effects, including pH, temperature and initial dye concentration (for aqueous dyes)/water pick‐up (for dyed cotton), on Reactive Blue FL‐RN, Reactive Red FL‐2BL and Reactive Yellow FL‐2RN, were studied via Fourier Transform–infrared spectroscopy, ultraviolet‐visible absorption and k/s curves. The kinetic results indicated that all the aqueous dyes were of pseudo‐first order kinetics, while dyed cotton represented pseudo‐secondary or pseudo‐third order kinetics. Based on their different mechanisms, there was a dramatic gap between dyes and dyed cotton in ozonation performance. The curve of rate constant vs pH of dye ozonation exhibited a U‐shape with the minimum value at pH 7, but for dyed cotton ozonation, the curve showed a Λ‐shape optimised at pH 10. Also, high temperature resulted in hydrolysis of dyes in aqueous solution, whereas this was not applicable to dyed cotton. The effect of pick‐up (0%‐150% with an interval of 75%) on reactive‐dyed cotton was greater than the effect of initial dye concentration (which varied from 120 to 40 mg/L) on the colour yield of aqueous reactive dyes.

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“…(3) its implementation is easy; (4) its network topology is no need to be determined in advance, which can be generated automatically when the training process terminates; (5) it has high generalized capability which can avoid local minimum [25]. Due to these prominent advantages, SVR has been demonstrated much success in the application in textile and fashion industry, such as prediction textile dying process parameters [26], yarns characteristics [27,28], fabric qualities [29], fabric contents [30,31] and human body measurements [32]. Hence, we adopted SVR to deal with the rules of GP associate adaptation in this study.…”

Section: Introductionmentioning

confidence: 99%

Construction of Garment Pattern Design Knowledge Base Using Sensory Analysis, Ontology and Support Vector Regression Modeling

Wang¹,

Zeng²,

Tao³

et al. 2021

IJCIS

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Garment pattern design is an extremely significant factor for the success of fashion company in mass customization and industry 4.0. In this paper, we proposed a new approach for constructing a garment pattern design knowledge base (GPDKB) using sensory analysis, ontology and support vector regression (SVR) modeling, aiming at systematically formalizing the complete knowledge on garment pattern design and realizing garment pattern associated adaptation. This approach has been described and validated in the scenario of personalized men's shirt design. The GPDKB consists of three components: conceptual knowledge base, relationship knowledge base and adaptation rules knowledge base. After selecting the optimal garment patterns using data twins-driven technique, the GPDKB has been built by learning from quantitative relationships between garment structure lines, controlling points and garment patterns and then simulated for pattern parameters prediction and pattern associate adaptation. Finally, the performance of the presented approach was compared with other classical data learning techniques, i.e., multiple linear regression and backpropagation-artificial neural network. The experimental results show that SVR-based approach outperform another two techniques with the lowest average of mean squared errors (0.1279) and average of standard deviation (0.1651). And the adaptation effect of GPDKB is equivalent to existing grading method. The general principle of the proposed approach can be adapted to creation of design knowledge bases for other type garments such as compression leggings. In fashion industry, the proposed GPDKB can effectively support designers by rapidly, accurately and automatically predicting relevant pattern adaptation parameters during garment pattern design.

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Modeling color fading ozonation of reactive-dyed cotton using the Extreme Learning Machine, Support Vector Regression and Random Forest

Cited by 19 publications

References 36 publications

Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning

Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning

A comparative study of ozonation on aqueous reactive dyes and reactive‐dyed cotton

Construction of Garment Pattern Design Knowledge Base Using Sensory Analysis, Ontology and Support Vector Regression Modeling

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