ArticlePolymers2023
Prediction of Electrical Resistance with Conductive Sewing Patterns by Combining Artificial Neural Networks and Multiple Linear Regressions.
Article in Polymers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Machine Learning in Polymeric Technical Textiles: A Review.Polymers · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to estimate the impact of sewing thread patterns on changes in the resistance of conductive yarns coated with silver paste. Firstly, the structure of the conductive yarns was examined, and various variations in the length and angle of individual sewing stitches were observed and analyzed through experiments. The results revealed that as the length of an individual stitch decreased, the width of the conductive yarn increased. Additionally, variations in the stitch angle resulted in different resistance values in the conductive yarn. These findings provide essential information for optimizing sewing patterns and designing components. Secondly, the comparison between models using multiple linear regression analysis and sewing neural networks was included to show optimized resistance prediction. The multiple linear regression analysis indicated that the stitch length and angle were significant variables affecting the resistance of the conductive thread. The artificial neural network model results can be valuable for optimizing sewing patterns and controlling resistance in various applications that utilize conductive thread. In addition, understanding the resistance variation in conductive thread according to sewing patterns and using optimized models to enhance component performance provides opportunities for innovation and progress. This research is necessary for the textile industry and materials engineering fields and holds high potential for practical applications in industrial settings.
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Registered trials
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 Socratic graph.