Detection and Separation of Conductive Traces

: for Smart Textile Recycling

Abstract

Smart textiles (e-textiles) integrate electronic components into fabrics to provide interactive functionalities, but this innovation creates new challenges at end-of-life. Embedded conductive traces and devices make it difficult to recycle smart textiles through conventional methods. This project addresses the recycling of smart textiles by developing a system to separate conductive elements from the fabric using a mixed sensor approach. An optical analysis module (embedded vision camera) identifies conductive threads on the textile via image processing, detecting their distinct line patterns, and an impedance measurement module electrically verifies the presence and continuity of these conductive traces. In the proposed system, an Arduino-based vision unit and an impedance sensor are mounted on a motion platform to scan discarded e-textiles. The vision system uses edge detection to locate thin, conductive pathways, while the impedance sensing probes confirm those pathways even when they are partially hidden or made of nonmetallic conductive material. Experimental results show that this dual-sensor method can reliably detect and localize conductive yarns in various smart textile samples. Once identified, the conductive traces are selectively removed (for example, cut out using a laser) from the textile. The combined approach improves detection accuracy compared to using vision or electrical sensing alone, ensuring that even low-visibility or broken traces are caught. This has important implications for sustainable e-textile disposal: by automating the identification and removal of electronic components from textiles, the system facilitates proper material separation, recovery of valuable conductive materials, and prevention of e-waste contamination in the textile recycling stream.

Date of Award	2025
Original language	English
Supervisor	Josef Langer (Supervisor)