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Deep Learning-based Data Registration of Melt-pool-monitoring Images for Laser Powder Bed Fusion Additive Manufacturing by Kim, Jaehyuk (author);Yang, Zhuo (author);Ko, Hyunwoong (author);Cho, Hyunbo (author);Lu, Yan (author) is a Engineering article available to read on EtoBox.
What is Deep Learning-based Data Registration of Melt-pool-monitoring Images for Laser Powder Bed Fusion Additive Manufacturing about?
Melt-pool monitoring (MPM) has been widely used in the laser powder bed fusion (LPBF) additive manufacturing process for process control and part quality prediction. Achieving this requires appropriate MPM-data alignments to the process parameters, part inspection, and testing data. This task is challenging when laser positioning is not synchronized with MPM-data acquisition. To address this challenge, this paper proposes a novel deep learning-based methodology to directly estimate melt pool positions in the machine-build coordinate system from the melt-pool images. First, we present two image-preprocessing methods, including image transformation and spatter reduction, using a stacked convolutional autoencoder to reduce noises from raw MPM images. Second, a convolutional neural network (CNN) based on self-supervised learning (SSL) is developed to identify the scan direction associated with an individual MPM image. Finally, Kalman Filtering is applied to estimate melt-pool positions given their scan directions. A case study based on an experimental build shows that the SSL-based CNN predicts the associated scan directions from the MPM images with an average error of about 1.37 ∘ , a
Who reads Deep Learning-based Data Registration of Melt-pool-monitoring Images for Laser Powder Bed Fusion Additive Manufacturing?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Kim, Jaehyuk (author);Yang, Zhuo (author);Ko, Hyunwoong (author);Cho, Hyunbo (author);Lu, Yan (author)
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)