An Analytic Approach for Acoustic Material Testing

Abstract

Acoustic material testing is well known for quality inspection of metal and composite parts in serial production with short cycle times and a robust performance. It is a referencing as well as a comparative testing method. Preclassified reference parts are required to teach the testing algorithm. The teaching process of an acoustic material testing system (ART) for suitable test criteria has to be done iterative by re-assessment and optimisation to improve the testing characteristics and the hit rate. Common acoustic testing systems are using conventional characteristics like frequency position, damping factor and amplitude monitoring or amplitude attenuation. But this is very often not enough for a reliable testing in consideration of batch or production tolerances. Modern ART systems offer already a choice of intelligent algorithms in combination with characteristics based on pattern recognition like frequency split up, frequency distance relations, multiple frequency peak detection, compensation and more. An additional approach is the evaluation of a complete frequency range instead of individual frequencies. Such characteristic attributes are more known for NVH testing like cumulative or average level, square sum and flatness tolerance. Self-learning test system is taken care with the aid of mathematical pattern recognition processes and automatic re-assessment feedback which will increase the acceptance and reliability of such acoustic material testing systems. Innovative aspects The innovative aspect is the application of pattern recognition techniques which leads to the future implementation of artificial intelligence technologies. With these new approaches, the acoustic testing will become much more usable for e.g. composite materials.