Modeling of a force sensitive resistance sensor into a weight load cell using artificial neural networks and adaptive neural - fuzzy logic inference system (ANFIS) and force - pressure control on fabric with fuzzy logic controller

Weight load cells containing tension sensors are modules capable of measuring weight. However, although weight load cells are suitable for normal weight measurements, they are not sufficiently small in terms of volume and are therefore not sufficiently useful in the automation process of a machine that requires weight measurement. However, force-sensitive resistive sensors are more useful than load cells due to their flat and thin structure. In this study, data obtained from force-sensitive resistive sensors with different characteristics were adapted to load cells using artificial neural networks and an adaptive neural-fuzzy inference system (ANFIS), and a model was created. During the training of the model, the data obtained from the sensor with the same weight was used as the input variable, and the data obtained from the load cell was used as the output variable. It was determined from the results graphs that the model created with ANFIS contained fewer errors than the model obtained with artificial neural networks and therefore created a better model. The model prepared with ANFIS was used in a fuzzy logic control system. In the fuzzy logic control system, a reference input was applied in grams, and the output was made to follow the reference value applied at the input. Subsequently, a fabric machine using a force-sensitive sensor was tested, and it was observed that the stepper motor driver provided control at the desired weights.