The microstructures of laser welds of the near-β titanium alloy TLM were studied for substrates in the as hot rolled, solution treated and solution treated and aged conditions. Irrespective of substrate condition the fusion zone consists of a cellular β structure. Trends in bulk hardness of the various conditions seem to be carried through into the weld consistently across the profile. The hardness in the fusion zone remained similar to that of the bulk. However, a drop in hardness occurs in the HAZ. Lath that formed around the hardness indent of the fusion zone and HAZ suggest that the weld undergoes a martensitic transformation to α" during deformation.
This paper outlines the current state of research into laser welding of titanium and its alloys for medical applications. The differences that exist between the medical and other industries are described and a direction for advancing research in this field is proposed.
To assess how tooth microstructure and composition might facilitate the pharyngeal mill mechanism of halfbeaks, apatite structure and iron content were determined by scanning electron microscopy and energy dispersive X-ray analysis for Hyporhamphus regularis ardelio, Arrhamphus sclerolepis krefftii, and Hemiramphus robustus. Iron was present in developing teeth and was concentrated along the shearing edge of spatulate incisiform teeth, which dominate the occlusive wear zone in all three species. A model based on tooth structure and wear rate is proposed to explain how halfbeaks maintain a fully functional occlusion zone throughout growth and consequent tooth addition and replacement. Replacement teeth erupt and wear rapidly so that a constant occlusion plane is always present. Iron within the tooth tissue reduces the wear rate of the cutting edge while simultaneously maintaining its sharpness and efficiency.
The manufacture of In Vitro Fertilization (IVF) needles is subject to the most stringent quality demands. This makes automated inspection challenging due to difficulty in reliably classifying conforming and non-conforming (defective) products due to factors including multidimensional variation of their tip geometry and the lack of an explicit quality standard. In addition, developing an IVF needle image dataset, which broadly contains the visual characteristics of qualified and defective products, is difficult without commissioning large and costly production runs. The most important original contribution of this work is a new solution to investigate and quantify the uncertainty in the quality standard of IVF needles by integrating inter-disciplinary techniques. This work utilizes a low-cost, virtual dataset of synthetic images, generated by the automated photo-realistic rendering of a three-dimensional (3D) parametric model to simulate manufacturing variation. Then, the unknown numerical (critical) quality thresholds are obtained by estimating the relationship between quality response and measurement predictors using an Ordinal Logistic Regression (OLR) algorithm on the synthetic images. The fitted models exhibited increased overall predictive accuracy of up to 11.02% than the machine learning models (available in MATLAB) and could provide objective guidance on classifying specific quality aspects of a product.
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