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Can I read Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts on EtoBox?
Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts by Simon Bellens; Gabriel M. Probst; Michel Janssens; Patrick Vandewalle; Wim Dewulf is a Materials Science article available to read on EtoBox.
What is Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts about?
Laser sintering is evolving towards a genuine manufacturing technique for volume production and mass customized products. However, variability in part quality has to be reduced further to enable its use for high demanding and critical end-use applications. High volume and mass customized manufacturing impose the need for a fast and flexible measurement instrument, to automatically assess the overall part quality, which is currently not available for the AM industry. XCT has shown to be an effective tool to measure the part quality, but the large acquisition time still obstructs the use of XCT for in-line quality inspections of laser sintered parts. Altering the XCT settings to decrease the total acquisition time influences the SNR and CNR of the reconstruction, introduces artefacts and directly influences the segmentation quality and feature analyses. To minimize the influence of the deteriorated image quality, deep learning segmentation algorithms are evaluated and compared with conventional segmentation and denoising algorithms on lowquality XCT scans with reduced acquisition times. The segmentation quality is quantitatively investigated with the Jaccard index, probability of det
Who reads Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts?
It is typically read by researchers, students, and practitioners in Materials Science.
- Author
- Simon Bellens; Gabriel M. Probst; Michel Janssens; Patrick Vandewalle; Wim Dewulf
- Publisher
- Elsevier BV
- Published
- 2022
- Language
- EN
- Field
- Materials Science (Physical Sciences)