This study is to investigate the improvement of quality and cost reduction of welding materials for combat vehicles through localization. The existing imported welding wires and localized welding wires were evaluated. The evaluation of the welding wires was performed for the feedability, chemical/mechanical properties, macro testing, and weldability. As a result of evaluation, the localized welding material met all the evaluation standard values. In addition, it showed excellent performance in terms of weldability and feedability. It is judged that the welding quality is improved thanks to the improvement of feeding characteristics through the localization of the welding material, and the fatigue of the worker is also expected to be relieved.

To estimate weld quality of the resistance spot-welding, the acoustic emission features are investigated from the total acoustic emission signal at the single-spot weld. Typically, the resistance spot welding process consists of several stages: set-down of the electrodes, squeeze, current flow, forging, hold time, and lift-off. Various types of acoustic emission response corresponding to each stage can be separately analyzed by using back-propagation neural network classifier and wavelet transform technique. The presented machine learning results provide a validation for using back-propagation neural network and wavelet transform technique as a valuable insights into the resistance spot-welding process. Especially, a wavelet transform technique is demonstrated and the plots are very powerful in the recognition of the acoustic emission features