Abstract
The microstructural properties of additively manufactured Ti–6Al–4V depend strongly on the heat treatment after Laser Powder Bed Fusion. Among others the evaluation of morphological characteristics of β-Ti precipitates allows a fast and objective way to distinguish between different metallurgical conditions with different mechanical properties based on an automated analysis of high-resolution SEM images. High temperature heat treatment and hot isostatic pressing improve the mechanical properties while also reducing the scattering of results, leading to better predictable performance. Similar and subjectively not distinguishable microstructural conditions are observed after applying low temperature heat treatments of different sample batches with comparable UTS of app. 1100 MPa but very different elongation at break between 0.3 % and 6.5 %. Advanced classification methods based on machine learning and classical statistics allow a reliable distinction of almost identical microstructural conditions. Their specific characteristics in terms of required expert domain knowledge, general performance and interpretability of their results are discussed.
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