Automated surface defect detection in forged parts by inductively excited thermography and magnetic particle inspection

Abstract

Inductively excited thermography has been shown to detect cracks in metallic components with good sensitivity. It is discussed as an alternative to magnetic particle testing. An open question to achieve acceptance in the industry is its testing reliability. A study with in total 200 forged steel parts was performed in order to compare the testing reliability of automated inductively thermographic testing and magnetic particle inspection. A robot supported thermographic inspection station was used. An inductor with orientation-independent crack detection was built up and tested. The thermographic phase images obtained were analysed by an automatic defect detection procedure based on machine learning techniques. Results of magnetic particle inspection served as a reference. Depending on the type of test object, an agreement of 68% to 82% was achieved, if only large indications of thermography were considered. The weak thermographic indications turned out to be due to shallow cracks (<150 µm depth). Improvement of the testing speed can be achieved by inspection inside large coils.