As a key component of weighing instruments, the load cell is a device that converts mass signals into measurable electrical signals for output. The accuracy and stability of its performance directly affect the reliability of the measurement results. To ensure the measurement accuracy of weighing instruments, calibration is required before use, that is, to determine the input-output characteristic relationship of the sensor. The traditional calibration method involves applying a known mass load to the weighing instrument using standard weights, while recording the output signal of the sensor. Through data processing, the calibration curve and parameters of the sensor are obtained.

However, this method has many drawbacks. For instance, it requires a large number of standard weights, the calibration process is complex, the cycle is long, and the cost is high. Moreover, for some weighing instruments installed in special positions such as high altitudes, underground, and large equipment, the loading and unloading of weights are extremely difficult, or even impossible. Therefore, the calibration method without weights has become an important topic in the field of weighing instrument research. Overview of the weight-free Calibration Method: The principle of weight-free calibration mainly relies on mathematical models and algorithm optimization, reference standard substitution, multi-parameter fusion and other means and methods to obtain the characteristics and rules of measuring equipment, thereby achieving the goal of not using traditional standard weights as the loading mass during the calibration process of weighing instruments, but simulating the mass load through other methods or means Or, by using relevant principles, algorithms and equipment, establish the corresponding relationship between the input and output of the load cell to achieve the purpose of calibration.

1.1 Mathematical Model and Algorithm Optimization: Establish an accurate mathematical model for the sensor, taking into account factors such as the sensor's physical characteristics, structural parameters, and working environment. Through the analysis and modeling of these factors, predict the output of the sensor under different input conditions. By applying advanced optimization algorithms such as the least square method and Kalman filtering, the parameters in the model are precisely estimated and adjusted to enhance the accuracy of calibration.

1.2 Reference standard substitution: Use high-precision known physical quantities or standard signals as references to replace traditional weights. For instance, a stable voltage source, current source or standard frequency signal is used to calibrate the relevant measuring equipment. Or, based on the output of a calibrated high-precision sensor of the same type as a reference, compare and calibrate the calibrated sensor.

1.3 Multi-parameter fusion comprehensively considers multiple parameters related to measurement, not relying solely on a single physical quantity. For instance, in the calibration of load cells, the influence of factors such as the position of the force application point and the loading speed on the measurement results should be taken into account simultaneously. By integrating the information of multiple parameters, a more comprehensive and accurate calibration model is established.