Evaluating geometric accuracy of embroidered QR codes for smart tag applications
Source abstract
Abstract
Embroidery offers a durable and attractive option for textile smart tags, but the effect of embroidery parameters on QR code accuracy and readability is still mostly unexamined, even in earlier studies. This study aims to evaluate the feasibility of using embroidery for functional QR code integration on woven and knitted fabrics by assessing the geometric accuracy of the Finder Pattern element, as defined by ISO/IEC 18004:2024. The analysis focusses on how module size, fabric type, stitch type and scan line direction affect dimensional stability. The experimental results showed that the deviations in the modules were systematic in all analysed variables. The most accurate geometry was achieved with an 8.8 mm Finder Pattern size, yielding negligible deviation (≈0%). Woven fabric and vertical scan line direction of knitted fabric demonstrated minimal distortions (–2% to +2.6%), whereas horizontal scan line direction for knitted fabric showed up to four times greater deviations (–8.8%). Stitch type influenced accuracy differently: satin stitch ensured precise shaping of individual modules, while tatami stitch preserved the overall Finder Pattern size. Statistical ANOVA analysis confirmed the most significant variables, with size being the dominant factor affecting total Finder Pattern length deviation. The research demonstrated that the Finder Pattern assessment method based on ISO/IEC 18004:2024 could be used to predict the quality of an embroidered QR code. The results prove that embroidery can support functional QR code production when parameters are carefully optimised. Future research should address durability under wear and washing conditions and expand quality assessment beyond the Finder Pattern.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: QR Code Applications and Technologies · Textile materials and evaluations · Advanced Sensor and Energy Harvesting Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Mechanical Engineering and Design, Kaunas University of Technology, Lithuania
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