# Electrical Resistance Response to Strain in 3D-Printed Conductive Thermoplastic Polyurethane (TPU)
## 4 Discussion  
The results show a systematic and predictable correlation between strain and the electric resistance after the initial loading cycle. When observing the time dependent behavior, a secondary peak can clearly by observed, as previously reported by [PERSON] et al. [44]. The sensors do, however, show a repeatable parabolic strain dependent behavior after the initial loading cycle. As strain is applied, the electric resistance is reduced before the resistance increases with the strain. This repeatable behavior makes the material suitable for strain sensing applications, as the strain can be derived from the electric resistance. However, hysteresis is present, and a calibration model should consider the different responses during loading and unloading. This could complicate applications significantly. Based on the converging behavior, the material maintains its strain-sensing capabilities for a significant number of loading cycles. After approximately 250 loading cycles, the cyclic behavior seems to have reached a close to steady-state condition where drift is negligible. From this point, the sensor would be able to accurately give information about the current cyclic strain. It is reasonable to believe that this behavior could be maintained until fatigue failure occurs. Previous studies have shown that TPUs show excellent toughness and cyclic fatigue resistance, indicating that the material is suitable for cyclic loading cases [43]. For the 10-sample validation test, there is a small standard deviation in the convergence. They follow the same converging pattern, indicating that different sensors will be able to yield similar behavior. There would only be a small difference from sensor to sensor. Based on Figure 5, the standard deviation looks to be mainly a consequence of a difference in initial resistance. One might, therefore, be able to calibrate the sensor based only on the initial electrical resistance measured prior to loading. The sensor could also be suitable for measuring strains during the initial loading. As seen in Figure 4, the electric resistance response follows the same curve for all strains and is independent from the strain rate up to a strain of 0.5. One could, therefore, use the sensors in applications requiring strain measurements up to a strain of 0.5 without considering the strain rate. One should, however, take creep into consideration when using the sensor for applications. The sensor shows a significant degree of creep, with a substantial change in resistance during the first seconds after strain is applied. This might be a problematic feature when designing a calibration model for the sensor. As the sensor is fabricated with FFF, the sensors can be printed directly integrated into the actuators in the single-step fabrication. FFF has already shown itself to be a proven fabrication method for soft robots. NinjaTek Ed requires no extra equipment for printing. The sensors could, therefore, be printed into the structure using a multi-material compatible 3D printer, allowing for complex webs and geometries of strain sensing sensors. Continuing this research may make part monitoring in 3D-printed components easier, enabling lifetime and component failure prediction. This applies not only to soft robotic applications but also to more rigid components. A soft robotics sensor application requires an external microcontroller for calculating the strain based on the electric resistance. The data presented in this paper show that the sensor reaches a close to steady-state behavior after approximately 250 loading cycles and follows a repeatable behavior both before and after this point. It may, therefore, be possible to calibrate the sensor through a \"pre-activation\" phase of minimum 250 cycles before use; however, this may be highly impractical in actual application.