Conductive Yarn Properties and Predicting Machine Sewability

The objective of this research is to enable the engineered manufacturing of sewn and embroidered e-textiles. It is achieved by conducting sewability assessments of commercially available conductive yarns and providing optimal sewing parameters to ensure electrical performance and mechanical suitability. Our approach includes yarn sampling, measurements, sewing experiments, statistical modeling, and performance tests of sewn sensors. We have scrutinized a range of conductive yarns with different formation mechanisms and electrical conductivities. Highly conductive, flexible, and fine count yarns are of particular interest in this proposed research. The physical properties of selected conductive yarns have been characterized and sewing experiments have been followed to evaluate the machine sewability of these conductive yarns under diverse sewing conditions. Using multiple logistic regressions and machine learning, these empirical observations are generalized and sewability models are established.

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Publication Details

Journal
Eng—Advances in Engineering
Published
2026-02-03
DOI
https://doi.org/10.3390/eng7020070
Primary Topic
Textile materials and evaluations
Type
article
Field-Weighted Citation Impact
0.00

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article

Conductive Yarn Properties and Predicting Machine Sewability

Minyoung Suh, Rong Yin, Kavita Mathur, Rishika Vontela et al.
Eng—Advances in Engineering
Textile materials and evaluations
article

Conductive Yarn Properties and Predicting Machine Sewability

Minyoung Suh, Rong Yin, Kavita Mathur, Rishika Vontela, Keysi Barrios, Yu Chen
article en

Abstract

The objective of this research is to enable the engineered manufacturing of sewn and embroidered e-textiles. It is achieved by conducting sewability assessments of commercially available conductive yarns and providing optimal sewing parameters to ensure electrical performance and mechanical suitability. Our approach includes yarn sampling, measurements, sewing experiments, statistical modeling, and performance tests of sewn sensors. We have scrutinized a range of conductive yarns with different formation mechanisms and electrical conductivities. Highly conductive, flexible, and fine count yarns are of particular interest in this proposed research. The physical properties of selected conductive yarns have been characterized and sewing experiments have been followed to evaluate the machine sewability of these conductive yarns under diverse sewing conditions. Using multiple logistic regressions and machine learning, these empirical observations are generalized and sewability models are established.

Eng—Advances in EngineeringVol. 7(2)
North Carolina State University (US), Wilson College (US)
Armament Research, Development and Engineering Center
Openalex Percentile: Top 68%
Textile materials and evaluations
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