Automated detection of geometric defects on connecting rod via acoustic resonance testing

Fully automated defect detection and classification of automobile components are crucial for solving quality and efficiency problems for automotive manufacturers, due to the rising wage, production costs and warranty claims. However, metrological deviations in form still represent unsolved problems using state-of-the-art techniques, especially for forged or casted components with complex geometry. In this paper, we attempt to overcome these challenges by using an acoustic resonance testing model that combines features extraction with defect classification from acoustic natural vibration signals. In this case the study doesn't focus on typical defects like cracks but on defective components in the sense of geometric configurations which are out of tolerance range. With an optimal feature extraction algorithm and a classifier training step, the proposed approach significantly accelerates the detection speed of unacceptable deviations in dimensions and parallely enhances the accuracy. The main contribution of this paper is that an optimal feature from acoustic signals is found which represents the geometric parameters most appropriately, meanwhile, the most appropriate classifier is obtained which significantly improves the efficiency and accuracy in defect classification