Machine Learning Algorithm Comparison for the Performance Evaluation of Manual Welding Process

Abstract

With the development of technology, the shipbuilding industry has seen a rise in automated welding processes. However, manual welding continues to be a necessary and in-demand skill. Unfortunately, the supply of both high-skilled and unskilled welders has steadily declined over the past decade, leading to a shortage of professional manpower. To address the issue of manpower shortage, a proposal has been made to provide timely and efficient training program for unskilled welders. However, evaluating the effectiveness of the training has proven challenging due to the lack of clear criteria for assessing the performance. This paper conducts an effective performance evaluation of unskilled welders using powerful machine learning algorithms. The evaluation is based on a dataset collected from both high-skilled and unskilled welder groups in manual welding processes. A comparison study is conducted to determine the most suitable algorithm. The goal is to identify the positive and negative parameters in the training of the manual welding process and provide an effective education strategy for unskilled welders. Additionally, the paper aims to find the algorithm with the highest accuracy. Overall, this study seeks to provide a solution to the shortage of manual welding professionals through the development of an efficient education strategy.