In recent years, knitted strain sensors have been developed that aim to achieve reliable sensing and high wearability, but they are associated with difficulties due to high hysteresis and low gauge factor (GF) values. This study investigated the electromechanical performance of the weft-knitted strain sensors with a systematic approach to achieve reliable knitted sensors. For two elastic yarn types, six conductive yarns with different resistivities, the knitting density as well as the number of conductive courses were considered as variables in the study. We focused on the 1 × 1 rib structure and in the sensing areas co-knit the conductive and elastic yarns and observed that positioning the conductive yarns at the inside was crucial for obtaining sensors with low hysteresis values. We show that using this technique and varying the knitting density, linear sensors with a working range up to 40% with low hysteresis can be obtained. In addition, using this technique and varying the knitting density, linear sensors with a working range up to 40% strain, hysteresis values as low as 0.03, and GFs varying between 0 and 1.19 can be achieved.

Harnessing the emergence of Industry 4.0 is challenging for small-and medium sized enterprises (SME). To overcome the challenges of collaborative robot (cobot) integration in SME manufacturing industry, TU Delft has founded the Cobot Learning Center (COLEAC). With a modular educational framework, future manufacturing workforce is (re)educated to the required skill levels. Trainees consist of SME professionals and multi-level educators. Next to cobot and AGV technology, Virtual and Augmented technologies are integrated into the courses and exemplary SME manufacturing processes are used in the classroom.

Smart textiles have been attracting considerable interest in imparting a wide range of functions to traditional clothing ranging from sensing, actuation, data processing, and energy storage. In the case of textile-based strain sensors, most of the studies proved that they can work in principle, however, producing strain sensors with desirable properties such as stable sensitivity, small hysteresis, large enough working range, and good repeatability still remains a challenge necessitating the developments of novel technologies for soft sensors. This paper conducts a systematic approach to investigate the electromechanical properties of the knitted strain sensors to find out the optimum process parameters. We found a repeatable and robust method to produce knitted strain sensors with low hysteresis at a working range of at least 40%.

How to visualize recorded production data in Virtual Reality? How to use state of the art Augmented Reality displays that can show robot data? This paper introduces an opensource ICT framework approach for combining Unity-based Mixed Reality applications with robotic production equipment using ROS Industrial. This publication gives details on the implementation and demonstrates the use as a data analysis tool in the context of scientific exchange within the area of Mixed Reality enabled human-robot co-production.