LINEAR REGRESSION-BASED CALIBRATION OF THE HK1100C PRESSURE SENSOR USING ARDUINO IDE

Pressure sensors play a crucial role in various industrial and scientific applications. The accuracy and precision of pressure measurements depend highly on the sensors' calibration. This study aims to calibrate the HK1100C pressure sensor using a linear regression method, supported by the Arduino IDE software, and to display real-time measurement results via an I2C LCD screen. The experimental procedure involves connecting the HK1100C sensor to an Arduino UNO board, collecting pressure data using a pressure gauge as a reference, and analyzing the data through linear regression to obtain the calibration equation. The results indicate that the HK1100C sensor provides high accuracy and precision. The smallest average error was recorded at 10 PSI with a value of 0.00038%, while the most significant average error occurred at 2 PSI with a value of 0.00372%. Additionally, the standard deviation of the measurements was relatively low, indicating good data consistency. The lowest standard deviation was found at 6 PSI at 0.0012%, and the highest at 12 PSI at 0.092%. The linear regression method effectively calibrated the HK1100C sensor, resulting in accurate and consistent pressure measurements. This approach also demonstrates that the Arduino IDE is an efficient and flexible tool for developing pressure measurement systems tailored to specific needs.