ArticleScientific reports2024
Modeling and optimization of dyeing process of polyamide 6 and woolen fabrics with plum-tree leaves using artificial intelligence.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Inverse input optimization for tuning two-ply yarn processing parameters using feedforward neural network.Scientific reports · 2026Article
- Optimization of cotton two-ply yarn parameters to enhance tensile strength using the grey wolf optimization algorithm.Scientific reports · 2026Article
- Multi-objective optimization of speed frame parameters for polyester spun yarn using artificial intelligence and grey relational analysis.Scientific reports · 2025Article
- Optimizing drawing frame variables to enhance polyester spun yarn quality using soft computing techniques.Scientific reports · 2025Article
- An attractive path to use of green resources for production of antibacterial and antioxidant wool yarns.Scientific reports · 2025Article
- Optimizing the eco-friendly dyeing of wool and nylon fabrics with Prangos ferulacea (L.) Lindl using artificial intelligence.Scientific reports · 2025Article
- Modeling peak ground acceleration for earthquake hazard safety evaluation.Scientific reports · 2024Article
- Free vibration optimization of 2D tri-axial braided composite fan blade with ANN-Anal-FEM-GA integrated model.Scientific reports · 2024Article
- A viscoelastic-plastic model for the core of various close-packings of multifilament polyamide-6 yarns.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The dyeing process of textile materials is inherently intricate, influenced by a myriad of factors, including dye concentration, dyeing time, pH level, temperature, type of dye, fiber composition, mechanical agitation, salt concentration, mordants, fixatives, water quality, dyeing method, and pre-treatment processes. The intricacy of achieving optimal settings during dyeing poses a significant challenge. In response, this study introduces a novel algorithmic approach that integrates response surface methodology (RSM), artificial neural network (ANN), and genetic algorithm (GA) techniques for the precise fine-tuning of concentration, time, pH, and temperature. The primary focus is on quantifying color strength, represented as K/S, as the response variable in the dyeing process of polyamide 6 and woolen fabric, utilizing plum-tree leaves as a sustainable dye source. Results indicate that ANN (R
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.