54 research outputs found

    Development of a modular system to provide confidence in porosity analysis of additively manufactured components using x-ray computed tomography

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    X-ray computed tomography (XCT) offers a promising non-destructive method to assess high value components that are additively manufactured (AM) for space-based imaging. However, AM components can be often challenging to measure and the true resolution of the XCT system used is both non-trivial to determine and may change locally. To solve this, we used high precision micro-machining to manufacture a cylindrical reference pin with internal holes. This pin can then be inserted into any component via subtractive machining, prior to the XCT process. A pre-existing AM flexure is modified to allow our modular system to be implemented. This allows XCT scanning and porosity analysis of similar components (similar geometry and manufacturing process) to be refined and adjusted based on the known internal micro-machined hole size. Analysis of the XCT volumetric data is implemented using a Python script developed for Avizo 2022.1, to compare and suggest the ideal threshold grey value (GV). The plugin threshold comparison is semi-automatic and 15 times faster than a manual comparison. Study findings showed how different calibrated micro-machined hole sizes (30 μm–120 μm) needed different thresholding values (188 GV–195 GV). Challenges and future studies related to traceability of the suggested method are discusse

    A data-driven approach for predicting printability in metal additive manufacturing processes

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    Metal powder-bed fusion additive manufacturing technologies offer numerous benefits to the manufacturing industry. However, the current approach to printability analysis, determining which components are likely to build unsuccessfully, prior to manufacture, is based on ad-hoc rules and engineering experience. Consequently, to allow full exploitation of the benefits of additive manufacturing, there is a demand for a fully systematic approach to the problem. In this paper we focus on the impact of geometry in printability analysis. For the first time, we detail a machine learning framework for determining the geometric limits of printability in additive manufacturing processes. This framework consists of three main components. First, we detail how to construct strenuous test artefacts capable of pushing an additive manufacturing process to its limits. Secondly, we explain how to measure the printability of an additively manufactured test artefact. Finally, we construct a predictive model capable of estimating the printability of a given artefact before it is additively manufactured. We test all steps of our framework, and show that our predictive model approaches an estimate of the maximum performance obtainable due to inherent stochasticity in the underlying additive manufacturing process. © 2020, The Author(s)

    Methods for Rapid Pore Classification in Metal Additive Manufacturing

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    The additive manufacturing of metals requires optimisation to find the melting conditions that give the desired material properties. A key aspect of the optimisation is minimising the porosity that forms during the melting process. A corresponding analysis of pores of different types (e.g. lack of fusion or keyholes) is therefore desirable. Knowing that pores form under different thermal conditions allows greater insight into the optimisation process. In this work, two pore classification methods were trialled: unsupervised machine learning and defined limits. These methods were applied to 3D pore data from X-ray computed tomography and 2D pore data from micrographs. Data were collected from multiple alloys (Ti-6Al-4V, Inconel 718, Ti-5553 and Haynes 282). Machine learning was found to be the most useful for 3D pore data and defined limits for the 2D pore data; the latter worked by optimising the limits using energy densities

    Additive manufacturing titanium components with isotropic or graded properties by hybrid electron beam melting/hot isostatic pressing powder processing

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    A methodology has been demonstrated to consolidate Ti-6Al-4V powder without taking it to the liquid state by novel combination of the electron beam melting additive manufacture and hot isostatic pressing processes. This results in improved static mechanical properties (both strength and yield) in comparison to standard EBM processed material. In addition, the ability to generate microstructurally graded components has been demonstrated by generating a component with a significant change in both microstructure and mechanical properties. This is revealed by the use of electron backscattered diffraction and micro hardness testing to produce maps showing a clear distinction between materials consolidated in different ways. The variation in microstructure and mechanical properties is attributed to the different thermal history experienced by the material at different locations. In particular, it is found that the rapid cooling experienced during EBM leads to a typical fine α lath structure, whereas a more equiaxed α grains generated by diffusion is found in HIP consolidated powder
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