Hao Wen

Laser powder bed fusion (LPBF)-based additive manufacturing (AM) has the flexibility in fabricating parts with complex geometries. However, using non-optimized processing parameters or using certain feedstock powders, internal defects (pores, cracks, etc.) may occur inside the parts. Having a thorough and statistical understanding of these defects can help researchers find the correlations between processing parameters/feedstock materials and possible internal defects. To establish a tool that can automatically detect defects in AM parts, in this research, X-ray CT images of Inconel 939 samples fabricated by LPBF are analyzed using U-Net architecture with different sets of hyperparameters. The hyperparameters of the network are tuned in such a way that yields maximum segmentation accuracy with reasonable computational cost. The trained network is able to segment the unbalanced classes of pores and cracks with a mean intersection over union (mIoU) value of 82% on the test set, and has reduced the characterization time from a few weeks to less than a day compared to conventional manual methods. It is shown that the major bottleneck in improving the accuracy is uncertainty in labeled data and the necessity for adopting a semi-supervised approach, which needs to be addressed first in future research.

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

This paper evaluated the effects of laser beam irradiation based surface modifications of Ti-10Mo alloy samples under either Ar or N2 environment to the corrosion resistance and cell integration properties. The surface morphology and element distributions of Ti-10Mo samples after laser surface treatments were evaluated using a scanning electron microscope, energy-dispersive x-ray spectroscopy, and x-ray photoelectron spectroscopy. Phase analysis was performed with x-ray diffraction. The electrochemical behavior of the Ti-10Mo samples was evaluated in simulated body fluid maintained at 37 ± 0.5 °C, and a cell-material interaction test was conducted using the MLO-Y4 cells. Laser surface modification in the Ar environment enhanced corrosion behavior, but did not affect the surface roughness, element distribution, or cell behavior, compared to the untreated samples. Processing the Ti-10Mo alloy in N2 formed a much rougher TiN surface that improved both the corrosion resistance and cell-material integration compared with the other two conditions.