Development of Neural Network based Plasma Monitoring system for Laser Welding Quality analysis

초록

Neural networks are shown to be effctive in being able to distinguish incomplete penetration-like weld defects by directly analyzing the plasma which is generated on each impingement of the laser on the materials. The performance is simililar to that of exsisting method based on extracted feature parameters. In each case around 93% of the defects in a database derived from 100 artificialy produced defects of kwon types can be placed into one of two classes: incomplete penetration and bubbling. The present method based on classification using plasma is faster, and the speed is sufficient to allow on-line classification during data collection.