Hand Motion Analysis for Recognition of Qualified and Unqualified Welders using 9-DOF IMU Sensors and Support Vector Machine (SVM) Approach

Abstract

This research aimed to find out how to identify qualified and unqualified welders of shielded metal arc welding (SMAW) in the shipyard industry. A cost-effective system that can identify the welder skills in real time is needed to reduce the cost of inspection and to maintain weldment quality. In this study, 9-degree of freedom (DOF) sensors of the inertial measurement unit (IMU) were applied to measure and to record the typical hand motions of welders. These sensors consisted of an accelerometer, a gyroscope, and a magnetometer installed in a microcontroller board, known as a wearable device. The wearable device was fitted on a welder's hand to monitor and to record wrist-hand motions of both qualified and unqualified welders. The data on inertial measurements of the welder's hand motions were sent through a Bluetooth connection and then saved in a memory card of a smartphone. Some properties, such as the root mean square (RMS), correlation index, spectral peaks, and spectral power, were extracted from the time-series data to characterize hand motions. The support vector machine (SVM) method, a part of the artificial intelligence (AI) technique, was applied to classify and to recognize the typical hand motions of the two types of welders using a supervised learning approach. The validation results showed that the proposed system was able to identify qualified and unqualified welders.