Ring spun yarn quality prediction using hybrid neural networks-Reference-Cited by-同舟云学术

Ring spun yarn quality prediction using hybrid neural networks

Published:2021-12-31 Issue: Volume: Page:1-9
ISSN:0040-5000
Container-title:The Journal of The Textile Institute
language:en
Short-container-title:The Journal of The Textile Institute

Author:

Ghanmi Hanen¹²,Ghith Adel²³,Benameur Tarek¹

Affiliation:

1. Laboratory of Mechanic Engineering, University of Monastir, National Engineering School, Monastir, Tunisia

2. National Engineering School, Department of Textile Engineering, Monastir, Tunisia

3. Research Unit Automatic Image and Signal Processing, University of Monastir, National Engineering School, Monastir, Tunisia

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Polymers and Plastics,General Agricultural and Biological Sciences,Materials Science (miscellaneous)

Link

https://www.tandfonline.com/doi/pdf/10.1080/00405000.2021.2022826

Reference24 articles.

1. Predicting Worsted Spinning Performance with an Artificial Neural Network Model

2. Fuzzy Neural Network Approach to Classifying Dyeing Defects

3. Cheng, L. & Adams, D. L. (1995). Yarn strength prediction using neural networks: Part I: 500.

4. Predicting compression and surfaces properties of knits using fuzzy logic and neural networks techniques

5. Ethridge, M. D. & Zhu, R. (1996). Prediction of rotor spun cotton yarn quality: a comparison of neural network and regression algorithms. Proc of the Beltwide Cotton Conference, 2, 1314–1317.

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. E-YQP: A self-adaptive end-to-end framework for quality prediction in yarn spinning manufacturing;Advanced Engineering Informatics;2024-10

2. Sensitivity analysis-based quality optimization strategy for multi-correlation parameters in spinning process;Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering;2024-08-28

3. Complementary knowledge augmented multimodal learning method for yarn quality soft sensing;Engineering Applications of Artificial Intelligence;2024-07

4. A two-layer stacking regression model for predicting yarn quality of small samples;Textile Research Journal;2024-01-31