Selection of optimal fabrication parameters of an innovative pressure sensor using fuzzy-AHP method based on sensor characteristics for robotic gripper

With increasing demand for diverse sensors across various applications, the development of innovative sensors is crucial to advancing emerging technologies. Fabric-based sensors, valued for their flexibility and softness, are gaining popularity in fields like medicine and healthcare. This study presents a new method for designing and fabricating a textile-based resistive pressure sensor using pad printing and conductive inks, specifically for robotic grippers. Key fabrication factors include fabric type, ink composition, and the number of print passes. Sensor performance was assessed based on sensitivity, linearity, repeatability, and fatigue resistance. The main goal was to identify and optimize fabrication parameters to achieve the best performance within a robotic gripper using the fuzzy-AHP (Fuzzy Analytic Hierarchy Process) method. The study was carried out in two phases. First, the optimal sample was chosen by evaluating general sensor properties through fuzzy-AHP modeling. In the second phase, to enhance the gripper's performance when handling objects with varying surface hardness, the model incorporated sub-criteria of sensitivity and linearity across different ranges. Three scenarios were developed to identify the best sensor sample for gripping objects of low, medium, and high hardness. Across four rounds of fuzzy-AHP modeling, the sensor printed on polyamide-based taffeta label fabric with three print passes and 100% carbon nanoparticle ink delivered the best overall performance, surpassing other configurations.