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The microstructural characteristics of spherical metal powders play an important role in determining the quality of mechanical parts manufactured by powder metallurgy processes. Identifying the individual powder particles from their microscopic images is one of the most convenient and cost-efficient methods for the analysis of powder characteristics. Although numerous image processing algorithms have been developed for automating the powder particle identification process, they perform less accurately in identifying adjacent particles that are heavily overlapped in their image regions. We propose an automatic algorithm to robustly and accurately identify spherical powder particles, especially heavily overlapped particles, from their microscope images. A parallel computing framework is designed to further enhance the computational efficiency of the proposed algorithm. Powder characteristics such as particle size distribution and the location of potential satellite particles have been derived from our identification results. The accuracy and efficiency of our algorithm are validated by real-world scanning electron microscope images, outperforming other existing methods and achieving both precision and recall above 99%.

This study presents the development of a novel MATLAB-based virtual prototyping tool called Will It Print that evaluates part geometry to check for compliance with design-for-additive-manufacturing guidelines relating to manufacturability. Specifically, the tool analyzes the part geometry for potential problems regarding warping, toppling, poor surface finish, and small or overhanging features when the part is produced using fused-filament fabrication. This tool helps designers evaluate the manufacturability of their parts and provides suggestions to change part geometry and orientation to avoid print failures and improve part quality. In this study, Will It Print was used to redesign several models and to choose a build orientation for 3D printing. The original and redesigned models were printed and compared. The redesigned models had lower scrap rates and improved quality. Our open-source MATLAB tool enables novices to engage in virtual prototyping for 3D printing so they can print high-quality parts without inefficient trial-and-error printing. This tool will be especially helpful for students and practitioners with limited access to a 3D printer, such as in remote learning modalities, which have become prevalent in recent years.

Resin-rich areas (a.k.a. resin-rich zones or resin pockets) are a common phenomenon in fiber-reinforced polymer (FRP) composites that significantly impact composite material properties and failure initiation. Although resin-rich areas can be intuitively observed from composite cross-sectional images, it is challenging to automate identifying them due to the absence of an explicit definition of their boundaries. In this paper, a novel algorithm is proposed to automatically identify resin-rich areas from composite cross-sectional images. We apply the concept of alpha-hulls to formalize a mathematical definition of the boundaries of resin-rich areas, and design an efficient process to compute the defined boundaries in seconds. This approach enables us to provide statistical analyses to quantitatively summarize the identified resin-rich areas, such as the number of separated areas and the histogram of their sizes. The rigorous mathematical definition of resin-rich areas and ability to collect thorough statistics will facilitate better understanding and quantification of the relationship between resin-rich areas and material properties.

The impact of topological defects associated with grain boundaries (GB defects) on the electrical, optical, magnetic, mechanical and chemical properties of nanocrystalline materials1,2 is well known. However, elucidating this influence experimentally is difficult because grains typically exhibit a large range of sizes, shapes and random relative orientations3-5. Here we demonstrate that precise control of the heteroepitaxy of colloidal polyhedral nanocrystals enables ordered grain growth and can thereby produce material samples with uniform GB defects. We illustrate our approach with a multigrain nanocrystal comprising a Co3O4 nanocube core that carries a Mn3O4 shell on each facet. The individual shells are symmetry-related interconnected grains6, and the large geometric misfit between adjacent tetragonal Mn3O4 grains results in tilt boundaries at the sharp edges of the Co3O4 nanocube core that join via disclinations. We identify four design principles that govern the production of these highly ordered multigrain nanostructures. First, the shape of the substrate nanocrystal must guide the crystallographic orientation of the overgrowth phase7. Second, the size of the substrate must be smaller than the characteristic distance between the dislocations. Third, the incompatible symmetry between the overgrowth phase and the substrate increases the geometric misfit strain between the grains. Fourth, for GB formation under near-equilibrium conditions, the surface energy of the shell needs to be balanced by the increasing elastic energy through ligand passivation8-10. With these principles, we can produce a range of multigrain nanocrystals containing distinct GB defects.

This work describes an experimental study to assess if analytical and empirical models can estimate the risk of warping deviation for parts made using fused filament fabrication based on part geometry. We also examine how the accuracy of the prediction varies for different machines and materials. If the predictive models can estimate risk of warping for a given part geometry, they can help enable better design for additive manufacturing so that designers can change part geometry early in the design process to have more easily-manufacturable parts, or choose an alternative orientation to optimize dimensional accuracy at the process planning stage. Specifically, we evaluate the extent to which two analytical models and one empirical model can assess the risk of warping for approximately rectangular parts with varying dimensions. We analyze dimensional accuracy data for parts with different length, height, and fillet type that were printed in ABS and PLA on different fused filament fabrication machines. After evaluating the three models, we found that the empirical model had the best performance over all datapoints. However, the analytical models showed promise but need further refinement on how the prediction of warping deviation depends on part height. Areas for additional research are highlighted.