Evaluating Manual vs. IoT-Driven Automated Calibration Methods for Scale Weights

Abstract

This study aims to compare manual calibration methods with automated calibration methods based on the Internet of Things (IoT) for scale weights, in accordance with the OIML R-51-1:2006 standards. The research employs three distinct types of scale weights: Cylindrical Knob, Cylindrical Hook Weight, and Cylindrical Slotted Weight. The comparison of these calibration methods is carried out using two key metrics: Correction Values and Standard Deviation Analysis. The experimental results indicate a trend where the accuracy of calibration diminishes as the weight of the scale increases. Specifically, when evaluating the three types of scale weights at a nominal test weight of 50 grams, the correction values obtained are relatively small, showing differences of 0.12, 0.1, and 0.02 for Cylindrical Knob, Cylindrical Hook Weight, and Cylindrical Slotted Weight, respectively. These findings suggest that the impact of weight on calibration accuracy is consistent across the types tested. In addition, Standard Deviation Analysis reveals that the IoT-based automated calibration method consistently achieves lower standard deviation values compared to the manual method. This indicates that the automated method provides more consistent and reliable calibration results. Overall, the study concludes that the IoT-based automated calibration method offers a significant improvement in performance over traditional manual methods for all three types of scale weights. This advancement highlights the potential for IoT technology to enhance the accuracy and efficiency of calibration processes in various industrial applications.