High activity levels in measurement laboratories can often exceed the time allocated for individual laboratory sessions. These laboratories involve a wide range of measurement tasks—from simple to complex—while students operate under strict time constraints. Consequently, the manual evaluation of student performance by instructors becomes labor-intensive and time-consuming. The digitization of the assessment process offers an effective solution. This paper presents a method that integrates LabVIEW with an SQL database to automatically record, verify, and grade student performance in laboratory sessions. The proposed method includes automated data saving for each student, validation of measurement accuracy, and objective grading mechanisms. Various tools are employed for real-time data acquisition, verification, and automatic evaluation of student activities.

This study focuses on the development of a system to effectively extract and utilize data from approximately 3,000 types of unstructured calibration certificates (e.g., paper, PDFs) widely used in the calibration industry. To process document-based data that are difficult for machines to recognize, the Korea Testing Laboratory (KTL) has developed a calibration certificate anomaly detection system called K-Argos, which applies object detection AI technology based on Convolutional Neural Networks (CNNs). The core function of the system is to extract data from unstructured documents, convert them into structured data, and standardize and store the results. Using the converted structured data, K-Argos provides statistical data management features such as performance analysis of calibrated equipment and anomaly detection in calibration data. Additionally, the system can be used to correct inaccurate identification information (e.g., manufacturer, model, serial number) and to analyze errors within the calibration data. In the future, the K-Argos system will be expanded to include an online technical supervisor service that automatically determines anomalies in data during the certificate approval stage and provides relevant information to the approver (technical supervisor). This is expected to significantly improve the efficiency and reliability of calibration tasks and contribute to the digital transformation of the calibration industry through the implementation of Digital Calibration Certificates (DCCs).

The introduction of standardized data formats for measurement and calibration data such as the digital calibration certificate (DCC) will enable further automation of calibration-related processes. This will open new possibilities for optimization of calibration processes in industrial applications. This paper describes some of the challenges and possibilities related to the transition to DCC-based calibration processes and management in the process industry. The findings presented are based on case examples and proof-of-concept projects from the process industry, in which automation of a calibration process is pursued using a combination of systems from multiple providers. The investigations and findings also suggest that parts of the DCC data structure could potentially be beneficial also for other applications, in which exchanging of calibration data is needed, but system integration is otherwise challenging due to lack of a suitable harmonized data structure.

We present automatic generation of Digital Calibration Certificate for a set of weights calibrated using a Robotic Mass Comparator operating in the mass range from 1 mg to 10 g. The tool features a Graphical User Interface that streamlines the processing of Digital Calibration Certificate from the automatically extracted and organized data in the calibration reports and the most recent calibration certificates of the reference standards and test weights adhering to relevant international recommendations, standards and guidelines. This user-friendly and highly automated software application represents a substantial advancement in the mass subfield of modern metrology in terms of handling large volumes of calibration data with minimal intervention.

Accurate modelling is important for both the performance control and monitoring of high-precision motion systems in advanced manufacturing machines. It is not difficult to build dynamic models for motion systems based on the multi-rigid-body system assumption. However, it is usually not feasible to acquire the real connection parameters between rigid bodies like stiffness and damping coefficients especially after mechanical assembly which can be significantly different from their designed values. In this paper, a measurement scheme using multiple accelerometers is presented with discussion of the data processing methods. Simulation results on a three-rigid-body motion system and experimental results on a two-mass motion system preliminarily verify the scheme. Finally, existing problems are put forward for further study.