Step heating thermography supported by machine learning and simulation for internal defect size measurement in additive manufacturing. (December 2022)

• Record Type: Journal Article
• Authors: Rodríguez-Martín, M.; Fueyo, J.G.; Pisonero, J.; López-Rebollo, J.; Gonzalez-Aguilera, D.; García-Martín, R.; Madruga, F.
• Is Part Of: Measurement. Volume 205(2023)
• Publication Date: 2022-12
• Subjects: Thermography -- Non-Destructive Testing (NDT) -- Quality control -- Additive manufacturing -- Machine learning; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.112140
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A novel method to measure internal defects in Additive Manufacturing is proposed. A thermography is assisted by machine learning to measure defects. Different regression algorithms were tested to generate the predictive models. The method is robust to the material's characteristics. Abstract: A methodology based on step-heating thermography for predicting the length dimension of small defects in additive manufacturing from temperature data measured on thermal images is proposed. Regression learners were applied with different configurations to predict the length of the defects. These algorithms were trained using large datasets generated with Finite Element Method simulations. The different predictive methods obtained were optimized using Bayesian inference. Using predictive methods generated and based on intrinsic performance results, knowing the material characteristics, the defect length can be predicted from single temperature data in defect and non-defect zone. Thus, the developed algorithms were implemented in a laboratory set-up carried out on ad-hoc manufactured parts of Nylon and polylactic acid which include induced defects with different sizes and thicknesses. Using the trained algorithm, the deviation of the predicted results for the defect size varied between 13% and 37% for PLA and between 13% and 36% for Nylon.