Analyzing the Effects of Plasma Treatment Process Parameters on Fading of Cotton Fabrics Dyed with Two-Color Mix Dyes Using Bayesian Regulated Neural Networks (BRNNs)

ABSTRACT

This study used Bayesian Regulated Neural Networks (BRNN) with 10-fold cross-validation to accurately forecast fading effects of plasma treatment on cotton fabrics for a given set of parameters. By training six independent BRNN models, a reduction in model complexity and an enhancement in generalizability to unknown datasets were achieved. The input comprises plasma treatment parameters and color measurements of the cotton fabric before fading, while the output comprises color measurements after fading. The plasma treatment parameters included color depth, air (oxygen) concentration, water content and treatment time. Color measurements included CIE L*a*b*C*h and K/S values. Furthermore, 162 datasets derived from two-color mixed-dye cotton fabrics were utilized for training and testing. The outcomes revealed superior prediction performance of the BRNN compared to the Levenberg-Marquardt Neural Networks, with R² values approaching 1 and 82.35% to 94.12% of the sample predictions lying within the acceptable color difference range. Through global sensitivity analysis, the impact of treatment parameters on fading effects was quantified, providing a scientific basis for parameter adjustment. This study not only elucidated the mechanism of plasma treatment-induced fading but also offers effective prediction tools for the intelligent and digital development of the fashion clothing fading domain.

摘要

本研究使用具有10倍交叉验证的贝叶斯调节神经网络（BRNN）来准确预测给定参数下等离子体处理对棉织物的褪色效果. 通过训练六个独立的BRNN模型，降低了模型的复杂性，增强了对未知数据集的可推广性. 输入包括等离子体处理参数和褪色前棉织物的颜色测量，而输出包括褪色后的颜色测量. 等离子体处理参数包括颜色深度、空气（氧气）浓度、含水量和处理时间. 颜色测量包括CIE L*a*b*C*h和K/S值. 此外，还利用162个来自双色混染棉织物的数据集进行训练和测试. 结果显示，与Levenberg-Marquardt神经网络相比，BRNN的预测性能优越，R2值接近1，82.35%至94.12%的样本预测处于可接受的色差范围内. 通过全局灵敏度分析，量化了处理参数对衰落效应的影响，为参数调整提供了科学依据. 这项研究不仅阐明了等离子体处理引起的褪色机制，而且为时尚服装褪色领域的智能化和数字化发展提供了有效的预测工具.

KEYWORDS:

• Bayesian regulated neural network
• 10-fold cross-validation
• sensitivity analysis
• plasma treatment
• two-color mix dye
• fading effect prediction

关键词:

• 贝叶斯调节神经网络
• 10倍交叉验证
• 灵敏度分析
• 等离子体处理
• 双色混合染料
• 衰落效应预测

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Hong Kong Polytechnic University [ZDCC].