HighlightsAn In situ quality monitoring for AM is presented Our approach combines acoustic emission with machine learning Fiber Bragg grating is used as acoustic sensors spectral convolutional neural networks is used for classification in terms of quality level The classification accuracy of the events ranged between 83 -89%.
In this work, a nominally new Fe‐Mn‐Si based shape memory alloy with a small amount of VC was designed. After an optimized thermo‐mechanical treatment, a shape recovery of more than 90% after an elongation of 4% could be achieved when the alloys were heated up to 225°C. In addition, high recovery stresses of up to 380 MPa could be obtained after heating to 225°C, whereas 330 MPa were obtained after heating to 160°C.
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A B S T R A C TThis paper describes a hybrid additive manufacturing process -3D Laser Shock Peening (3D LSP), based on the integration of Laser Shock Peening (LSP) with selective laser melting (SLM). The well-known tensile residual stresses (TRS) in the as -built (AB) state of SLM parts in the subsurface region have a detrimental effect on their fatigue life. LSP is a relatively expensive surface post treatment method, known to generate deep CRS into the subsurface of the part, and used for high end applications (e.g. aerospace, nuclear) where fatigue life is crucial. The novel proposed 3D LSP process takes advantage of the possibility to repeatedly interrupt the part manufacturing, with cycles of a few SLM layers. This approach leads to higher and deeper CRS in the subsurface of the produced part, with expected improved fatigue properties. In this paper, 316L stainless steel samples were 3D LSP processed using a decoupled approach, i.e. by moving back and forth the baseplate from an SLM machine to an LSP station. A clear and significant increase in the magnitude and depth of CRS was observed, for all investigated process parameters, when compared to the AB SLM parts, or those traditionally LSP (surface) treated.
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