Accurate Automation of Deadweight Force Standard Machine Based on Artificial Intelligent Model at NMCC-SASO-KSA

Accurate force measurements are critical in a various fields including metrology, material testing, and quality control. In this study, we propose an automated process for precise force measurements using Dead weight standard machines up to 1kN. This method leverages a combination of hardware and software components to achieve reliable results. The key steps in the automation process include the calibration Setup, which consists of a carefully calibrated force transducer used as a reference, which is selected based on its traceability to national standards. Automated Loading System (ALS) is the second item of the process, in which a motorized system places a known weights on the force transducer. The loading process was controlled to minimize disturbances and ensure accurate readings. The third step is Data Acquisition and Analysis, which includes a high-precision amplifier that collect force data during loading, process the acquired data, compensate for environmental factors (e.g., temperature and humidity) based on LabVIEW, and apply statistical analysis techniques to estimate the force values using python script based on classification machine learning. The full script is open source for all reader to apply in their different fields. The proposed automation process enhances measurement efficiency, reduces, cost, time, human errors, and ensures consistent results. This contributes to the advancement of force metrology and facilitates reliable force measurements in various applications.