Every year our customers and teams find interesting applications to investigate. Learn more some of this year's new insights uncovered through Robo-Met.3D®'s automated serial sectioning here. Contact us to learn more about how you can use Robo-Met to get the material insights you need today.
Cross-section images through the center axes of one cone series with the image location indicated in the 3D visualization on the right. (A) X-ray CT, (B) MPSS (Robo-Met), (C) CLSM.
Nondestructive characterization of internal features and defects within complex components is vital for many industrial applications, particularly with the advent of additive manufacturing (AM) technologies. However, community understanding of the limitations of nondestructive methods such as X-ray Computed Tomography (CT) can be limited in certain industrial sectors as these may be emergent applications. In this paper, we investigate the limits of X-ray CT measurements and compare extracted data with mechanical polishing serial sectioning (MPSS) and confocal laser scanning microscopy (CLSM). The test object is an additively manufactured titanium alloy disk that contains both process-induced porosity and machined features, including focused ion beam milled features designed to probe the resolution limits of X-ray CT. CT. Results show that each of these characterization techniques has advantages and disadvantages. We compare data acquisition times, spatial resolution, geometric measurement accuracy and defect visualization fidelity across these modalities to establish a practical framework.
Citation: Townsend, A. P., Draganic, N., Yee, C., Uchic, M., Sparkman, D., & Jolley, B. (2024). Comparison of Three Measurement Modalities for 3D Characterization of Manufactured Features and Native Porosity in Additively Manufactured Titanium Alloy Parts (No. LLNL-CONF-860726). Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States).
For in-situ measurement of the melt pool (MP) temperature profile in the laser powder bed fusion (LBPF) additive manufacturing (AM) process, a new technology is the single-camera two-wavelength imaging pyrometry (STWIP). Accurate temporally and spatially resolved MP temperature field measurement using this STWIP method requires a precise profiling of pixel-wise two-wavelength intensity ratio, which is highly dependent on optical alignment, and camera's spectral sensitivity, among other factors. Thus, it is essential to develop an accurate, robust, and fast transformation method for reliable and effective mapping of two-wavelength images acquired from the STWIP system. In this work we propose a Blob analysis-based MP guided Image Transformation (BMPIT) method as opposed to the typical feature detector descriptor-based image transformation approach like KAZE. The BMPIT's performance is assessed and compared with the KAZE in terms of efficiency, execution time, accuracy, and robustness. An experiment using a standard calibrated tungsten filament strip lamp is done to validate the effectiveness of BMPIT. Compared to the KAZE, the BMPIT successfully transformed 100 % of the MP images with higher accuracy and faster speed. It is also shown that the BMPIT is a robust technique for image transformation, unaffected by the image size, MP position, and surrounding noise. Moreover, experimental ground truth data collected using Type C thermocouples implanted into an Inconel-718 build plate are used to further validate the LPBF MP temperature estimation accuracy of BMPIT-aided STWIP. Unlike KAZE, temperature estimated by BMPIT agrees well (error <5 %) with both the lamp and thermocouple experiments. BMPIT is an appealing alternative for online measurement due to its reduced execution time, it takes only one fifth of the time that KAZE takes to transform two-wavelength images. In addition, the BMPIT can be used to calculate MP width, which is validated by comparing with ex-situ characterization. It enables a high level of agreement (with an error less than 1.89 %) between MP images of two wavelengths. Overall, the BMPIT greatly improves STWIP image processing, allowing for measuring MP temperature and morphology more rapidly, accurately, and precisely. The developed BMPIT approach can be employed as part of a STWIP-feedback LPBF process control system to improve the quality of additively manufactured metal products.
Citation: Alam, M. J., Zhang, H., & Zhao, X. (2025). Enhancing image processing in single-camera two-wavelength imaging pyrometry for advanced in-situ melt pool measurement in laser powder bed fusion. Precision Engineering, 93, 1-17.
Citation: Glaessgen, E. H., & Kitahara, A. R. Computational Materials-informed Qualification and Certification of Process-Intensive Metallic Materials.
Direct Ink Writing (DIW) combines the flexibility of 3D printing with increased material applications such as thermoset carbon fiber composites, ceramic composites, and metals. The usefulness of direct ink writing, like many additive manufacturing (AM) processes, remains limited for reasons ranging from quality control to lack of process parameter optimization. This study looks to introduce a methodology for characterizing direct ink written carbon fiber composites to facilitate exploration into the relationships between process parameters and material structure. The presented study utilized nine 3D specimens of direct ink writing carbon fiber composites printed with varying process parameters – speed differential, layer height, step-over distance, and nozzle diameter – as the data set. The data was collected with an automatic serial sectioning system, LEROY, from the Air Force Research Laboratory. The collected data was processed in DREAM.3D and analyzed with statistical comparisons of 2D orientation distributions of the fibers, 2D size distributions of the voids, and 2D shape distributions of the voids.
Citation: Clarke, K. M., Groeber, M., Wertz, J., Abbott, A., Haney, R., & Chapman, M. (2025). Characterization of Direct Ink Writing carbon fiber composite structures with serial sectioning and DREAM. 3D. Composite Structures, 353, 118730.
Citation: Chen, B., Li, D., Davies, P., Johnston, R., Ge, X., & Li, C. (2025). Recent Progress of Digital Reconstruction in Polycrystalline Materials. Archives of Computational Methods in Engineering, 1-52.
Mechanical serial sectioning (MSS) provides detailed microstructural information across large length scales. By repeatedly removing thin layers of material and imaging the exposed surface, a 3D representation of a specimen’s internal structure can be constructed, enabling failure analysis and feature identification that are otherwise inaccessible via conventional 2D or nondestructive evaluation techniques. Achieving consistent and accurate material removal can be challenging due to system variability, requiring an experienced operator to manually adjust parameters, prolonging data collection times and necessitating post-processing routines to standardize the data. To address these challenges, this paper presents the employment of a one-step model predictive control (MPC) framework tailored to a run-to-run (R2R) controller. The R2R-MPC controller automates the parameter selection process, improving the consistency of material removal through iterative feedback for disturbance rejection and accurate tracking of the target removal rate. Using a data-driven approach, the controller robustly adapts to changing material characteristics. The effectiveness of the R2R-MPC controller is demonstrated through simulation and experimental results and compared to previous data collection procedures.
Citation: Oakley, R. M., Chao, P., Danielson, C., & Polonsky, A. T. (2025). A Robust Data-Driven Approach for Mechanical Serial Sectioning. Integrating Materials and Manufacturing Innovation, 1-9.