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.
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