Early stage crack detection in mechanically joined steel/aluminum joints by condition monitoring

Monitoring systems for machines, plants, materials and equipment are increasingly used in production processes. These online condition monitoring systems can detect damage or excessive loads at an early stage and can drastically reduce or prevent long downtimes of plants and machines as well as high repair and maintenance costs. This paper depicts a method for online crack detection with pattern recognition methods for specimens joined by self-pierce riveting under cyclic load in fatigue tests (laboratory application). A software specially conceived for this application was developed. This software, AnrissMF, uses active acoustic testing with a structure-borne sensor to detect cracks in the joints at a very early stage. It is shown in this paper that this software can detect cracks much earlier than classical failure criteria for joints (i. e. before any drop in stiffness or frequency is observed). Furthermore, the successful application of software AnrissMF for online crack detection during the fatigue strength test is presented.