Search

X-ray computed tomography (XCT) is the only non-destructive technique able to perform a complete quality control of additive manufactured products in a single inspection. Yet, high-quality scans are associated with large acquisition times and limited to high-end AM parts. In this paper we investigate a deep learning U-net segmentation algorithm to improve the segmentation of low-quality XCT scans. A high-quality XCT scan is acquired and aligned with low-quality XCT scans to create the ground truth data. The accuracy of the segmentation is quantified with the Jaccard index and physical properties of the parts.

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 low-quality XCT scans with reduced acquisition times. The segmentation quality is quantitatively investigated with the Jaccard index, probability of detection, pore size distributions and porosity values and a qualitative comparison is provided. An improved segmentation for low-quality XCT scans is obtained by using deep learning segmentation algorithms while preserving a high generalization of the segmentation algorithm on low-quality XCT scans with a wide SNR and CNR range. ispartof: Polymer Testing vol:110 status: Published online