A deep learning-based approach for the automatic measurement of laser-cladding coating sizes

Abstract

Problems such as long consumed time, high requirements for experience, and large subjectivity exist in the extraction of dimensional data of each area of the cross-section in the measurement process of the geometrical morphology of the laser cladding coatings. These problems seriously affect the accuracy of the coating morphology acquisition. An automatic measurement method for laser cladding coating sizes was studied based on deep learning and image processing technology. First, improved semantic segmentation model VMA-Unet of Unet deep learning was proposed. Each area of the coating morphology image was accurately divided, with the mIOU reaching 95.10 %. The image processing technology was then used to process predicted image output by the semantic segmentation model to automatically output the key coating size. We found that the mean accuracy of the measurement results of the method for each key size reached more than 98 % through tests and verification. A new method was provided for the accurate acquisition of the morphology data of the laser cladding coatings after verifying the feasibility and effectiveness of the automatic measurement method.