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Additive manufacturing (AM) technology, also known as 3D printing, has transitioned from concepts and prototypes to part-for-part substitution and the creation of unique AM-specific part geometries. These applications are increasingly present in demanding, mission-critical fields such as medicine and aerospace, which require materials with certain thermal, stiffness, corrosion, and static loading properties. To advance in these arenas, metallic, ceramic, and polymer composite AM parts need to be free from discontinuities. The manufacturing processes have to be stable, robust, and repeatable. And the nondestructive testing (NDT) technology and inspection methods will need to be sufficiently capable and reliable to ensure that discontinuities will be detected to prevent the components from being accepted for use. As the second installment of a six-part series of SAE EDGE™ Research Reports on AM, this one discusses the need, challenges, technologies, and opportunities for NDT in AM.

Mass is one important suspicious object for breast cancer diagnosis in mammograms. Computer-aided detection (CAD) based on fully supervised deep learning achieves high performance for mass detection in mammograms. The lack of fine-grained expert labels becomes the bottleneck for the large-scale application of CAD to achieve detection in mammograms. Weakly supervised methods provide a solution to tackle the annotation problems, including in the application for mass detection. However, previous works face the problem of insufficient localization information, which affect the ability of mass detection. In this paper, we propose a multi-view enhancing mass detection network (MVMDNet) with dual view inputs that contains craniocaudal (CC) and mediolateral oblique (MLO) view of mammograms, where different view features are interacted and fused to enhance localization information.

The experimental investigation aims to improve natural composite materials aligned with feasible development principles. These composites can be exploited across several industries, including the automobile and biomedical sectors. This research employs date seed powder and neem gum powder as reinforcing agents, along with polyester resin as the base material. The fabrication route comprises compression moulding, causing the production of the natural composite material. This study focuses extensively on mechanical characteristics such as tensile strength, flexural strength, hardness, and impact resistance to undergo comprehensive testing. Furthermore, the chemical properties of the composites are examined using the FTIR test to gain understanding by integrating different proportions of date seed powder (5%, 10%, 15%, and 20%) and neem gum powder (0%, 3%, 6%, and 9%) in the matrix phase.