Acoustic-Emission-Analysis of Dissimilar Laser-welds of Aluminium and Polyamide 6.6

Abstract

A novel laser joining process for hybrid polymer-metal structures has proven a strong bond between polyamide and aluminium. This welding technique is strongly requested by the automobile producing industry for numerous applications within structure components. However, the joining quality exhibits a strong dependency on process-related variables, which highlights the need for an online inspection technique. The requirements for a chosen inspection technique are [1], [2]: 1. Showing results on-line during the joining process; 2. Working non-destructive; 3. Working as an integral method; Being able to determine weld defects. In that case only the Acoustic Emission Analysis (AEA) as a Non-Destructive-Technique (NDT) can be used to guarantee reasonable results. During the first tests at the welding laboratory at the Laser Technology Competence Centre (LTCC) at the University of Luxembourg, AEA sensors are applied onto the surface of the test specimen which have to be welded. In this principle acoustic events, caused by welding defects, are recorded during the laser joining. The most important laser process parameters, like velocity, power and horizontal focal position of the laser spot, have been verified and evaluated by the AEA. It was observed that insufficient laser power can lead to the break of the melting bath, which is often not recognizable by non-destructive optical methods [2]. The AEA however was able to detect this lack of fusion. Using high laser power, the appearance of gas bubbles arising from the Polyamide 6.6 could be detected afterwards in a microscopic micrograph. The AEA signals related to this effect can be correlated clearly. Due to the high pressure of the gas bubbles, the aluminium weld is often interrupted, which can be traced back to the solidification of the aluminium molten mass. Due to the actual situation, the AEA is a well-working NDT online monitoring method and can be used for the correlation of acoustic events and welding defects within the mentioned joining method. Future tasks will develop some algorithms to separate the different defects by pattern recognition of the AEA signals and parameter