Sen Liu

Laser powder bed fusion (LPBF) enables the fabrication of high-precision metal parts, but spatter can disturb the powder bed and contribute to defects. This study compared single-track LPBF of SUS316L stainless steel in air and low-oxygen N₂ (O₂ < 1%) using high-speed imaging with pulsed illumination. The temporal evolution, apparent size, and spatial distribution of detected particles were examined at different laser powers and scanning speeds. Scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM–EDS) was used to assess the laser-affected regions. The atmosphere-dependent trend varied with processing conditions: low-oxygen N₂ did not consistently reduce the detected particle count, whereas at 200 W and 500 mm/s, it showed fewer large-particle observations than air. The two atmospheres also produced different particle distributions relative to the moving laser. SEM–EDS showed lower O and Mn signals and a higher relative Fe fraction in the regions examined under N₂. These observations establish condition-dependent differences between the two gas environments, although the contributions of oxygen chemistry, gas flow, and optical differences between the experimental configurations cannot be separated.

Laser powder bed fusion (LPBF) is a key additive manufacturing technique for producing high-precision metal parts. However, spatter phenomena during the LPBF process can compromise part quality by inducing defects such as porosity and surface roughness. This study investigated the spatter behavior in air and nitrogen environments using high-speed imaging to analyze spatter ejection and its impact on powder bed conditions. The results showed that the spatter size and number were lower in nitrogen than in air. This provides insights into optimizing the process parameters to reduce defects and enhance the reliability and part quality of components manufactured via LPBF.

Fibrous materials are ubiquitous in modern technology and essential to diverse industrial applications spanning medicine, energy storage, advanced textiles, and structural composites. Quantitative morphometric analysis of these fibrous structures from microscopy images, however, remains challenging due to the complexity of discriminating the elongated and intertwined fibers, particularly in heterogeneous materials with varying fiber diameters and particulate matter. Traditional image analysis methods often fail to accurately characterize these mixed fiber–particle networks, and manual measurement approaches remain subjective, time-consuming, and prone to operator-dependent variability. We present a novel automated framework, the interactive Fiber Analyzer GUI, with free access to any interested parties, for multiscale morphometry analysis that integrates several key innovations: (1) a robust dual-pipeline discriminator that uses a filter-based approach for pure fiber networks and morphological filtering with aspect-ratio thresholding to effectively segregate components in mixed-morphology systems; and (2) Adaptive Contour-based Diameter Estimation (ACDE), utilizing active contour models for precise fiber diameter measurement at multiple skeletal points. The integrated pipeline demonstrates superior performance, achieving efficient implementation times (19–25 s per ROI) and significantly reduced error rates across validation samples (mean error 7.17% ± 1.29%, range 5.13–8.57%) for both pure fiber and complex mixed-morphology samples when combined with empirically optimized correction factors that compensate for systematic measurement bias in automated analysis. This framework advances automated morphometric analysis by providing accurate, reproducible quantification of complex fibrous networks.

This paper presents an integrated robotic fused deposition modeling additive manufacturing system featuring closed-loop thermal control and intelligent in-situ defect correction using a 6-degree of freedom robotic arm and an Oak-D camera. The robot arm end effector was modified to mount an E3D hotend thermally regulated by an IoT microcontroller, enabling precise temperature control through real-time feedback. Filament extrusion system was synchronized with robotic motion, coordinated via ROS2, ensuring consistent deposition along complex trajectories. A vision system based on OpenCV detects layer-wise defects position, commanding autonomous re-extrusion at identified sites. Experimental validation demonstrated successful defect mitigation in printing operations. The integrated system effectively addresses challenges real-time quality assurance. Inverse kinematics were used for motion planning, while homography transformations corrected camera perspectives for accurate defect localization. The intelligent system successfully mitigated surface anomalies without interrupting the print process. By combining real-time thermal regulation, motion control, and intelligent defect detection & correction, this architecture establishes a scalable and adaptive robotic additive manufacturing framework suitable for aerospace, biomedical, and industrial applications.

In this work, we describe the instrumentation used to perform the first operando transmission X-ray microscopy (TXM) and simultaneous X-ray diffraction of laser melting simulating laser powder bed fusion on the XCS instrument at the Linac Coherent Light Source (LCLS) X-ray free-electron laser (XFEL). Our TXM with 40× magnification in the X-ray regime at 11 keV gave spatial resolutions down to 940 nm per line pair, with effective pixel sizes down to 206 nm, image integration times of −1 but we did not observe any motion in this range in the laser melting of Al6061 alloy. Shockwaves were not observed and hot cracking proceeded at velocities below the detection limits. Pore accumulation was observed between successive shots, indicating that bubble escape mechanisms were not active. With proper experimental design, the spatial resolution, contrast and field of view could be further improved or modified. The increased brightness and narrower bandwidth of the XFEL allowed for this imaging technique and it lays the groundwork for a wide range of operando techniques to study additive manufacturing.