During the training of a welder, either novice or professional, most activities are focused on the acquisition of wrist-hand motion skills. In the basic welding training, trainees initially required hand-on practices to acquire the skills of wrist hand motion to maintain the distance of electrode tip to a base metal such that the welding arc was continuously flaming. Secondly, trainees were practices of manipulating hand motion to follow seam tracking for joining two metals within defined speed & torch height. These practices were then continued for various types of weld joints. The result of acquiring this skill level was then assessed by inspecting the visual appearance of the weldment. In this study, an effort was undertaken to monitor and assess the progress of acquiring wrist-hand motion skills using wearable sensors: accelerometer, gyroscope, and magnetometer. Then, the record of those sensors was plotted as a time series signal compared with those performed by the training instructor. Their achievement of skills grade was analyzed using the Supervised Vector Machine (SVM) Learning Method. The result has indicated that this proposed method can assist in assessing welder trainees' efforts to improve their skills.
|ジャーナル||IOP Conference Series: Earth and Environmental Science|
|出版ステータス||出版済み - 2月 4 2022|
|イベント||6th International Conference on Marine Technology, SENTA 2021 - Surabaya, インドネシア|
継続期間: 11月 27 2021 → …
!!!All Science Journal Classification (ASJC) codes