# Electrical Resistance Response to Strain in 3D-Printed Conductive Thermoplastic Polyurethane (TPU)
## 4 Discussion
Since the microcontroller would continuously monitor the sensor load case, an alternative to this solution is to extract an accurate strain already after the first loading cycle by calibrating the microcontroller for the predictable and repeatable sensor drift shown in Figure 5.  
Both calibration attempts presented in Section 2.2 were performed with the aim of fixing the ambiguity issue arising from the polymeric behavior of the resistance-strain curve. The attempts of pre-straining the sensor was unable to achieve this goal as a new parabolic curve emerged. This result simply created the same problem over again. However, the attempts of utilizing sensors with a difference in cross-section area gave potentially promising readings as a slight discrepancy between the two sensors occurred after a strain of 0.3, marking the parabolic curve minimum. This may be used for separating the readings before the curve minimum from after. The sensor sensitivity should also be calculated after a successful setup and calibration has been achieved. This should be conducted for the sake of the user and to compare the sensor with other work. Other solutions than the two presented in Section 2.2 are combining multiple angled sensors in a full bridge strain gauge setup to be able to extract an unambiguous strain value from the electric resistance response [46]. Also, the sensor may be combined with other sensors and trained using a machine learning algorithm. In addition, the sensor may still be used for other applications, such as a binary sensor generating an active/non-active response. There may be multiple factors affecting the sensor behavior that have not been identified. All tests were conducted with a voltage of 5 V. Variations in voltages and currents have not been tested. It is also possible that temperature may affect the resistance of the sensor. Furthermore, the tests have been restricted to cyclic testing, and we do not know the effect of more arbitrary strains on the sensor behavior. It is, however, not unlikely that this will drastically affect the sensor useability as the sensor is susceptible to both hysteresis and curve reshaping, as shown in Figure 7A. By adding sensors to products, we argue that smart components capable of providing user feedback, monitoring, and input for soft robotics could have a major impact on product development. In the process of FFF, now with an increasing availability of multi-material hardware, such sensors can be embedded internally into such components. The FFF process allows for three-dimensional sensor integration with no restrictions in terms of product complexity. FFF is also an excellent manufacturing method for products with complex geometries and specialized components with custom interfaces, and our proposed research is the first step in adding sensing capabilities into such products with relatively low effort and cost. In summary, we argue that the combination of conductive filament and the FFF process will enable sensing in products without a major increase in cost and, therefore, have a major impact on smart product designs.