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A Deep Learning Framework for Defect Prediction Based on Thermographic In-situ Monitoring in Laser Powder Bed Fusion by Simon Oster; Philipp P. Breese; Alexander Ulbricht; Gunther Mohr; Simon J. Altenburg is a Engineering article available to read on EtoBox.
What is A Deep Learning Framework for Defect Prediction Based on Thermographic In-situ Monitoring in Laser Powder Bed Fusion about?
Abstract The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers. Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. The model is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The imp
Who reads A Deep Learning Framework for Defect Prediction Based on Thermographic In-situ Monitoring in Laser Powder Bed Fusion?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Simon Oster; Philipp P. Breese; Alexander Ulbricht; Gunther Mohr; Simon J. Altenburg
- Publisher
- Springer Science and Business Media LLC
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)