Infrared Thermography and Image Processing applied on Weldings Quality Monitoring

The present paper proposes a methodological approach to evaluate welding quality in the context of tank production. In particular, infrared thermography was adopted to control the structural homogeneity achieved after welding. The analysis was implemented by applying the K-Means algorithm, morphological image processing and artificial neural network based on the Long Short Term Memory - LSTM - technique. The adopted approach was chosen to perform different image post-processing analysis and therefore highlights, identifies, and quantifies welding inhomogeneities, as cracks and defects. The work was developed within the research framework of an industrial project. The proposed approach could be implemented in inline production systems which integrate an artificial intelligence processor for real time quality monitoring.