santhosh

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  "apptext": "Uncertainty Quantification for Additive Manufacturing  In last years, Additive Manufacturing (AM) evolved from a rapid prototyping process into an industrial method to produce complex metallic parts for aerospace applications. AM is allowing designers to have an unprecedented level of flexibility and there are several critical applications where this technique can be disruptive. Even if it proved its potential on several aerospace components (General Electric nozzle fuel mixer), its applicability is limited to non-critical parts with low load and working in no so harsh conditions.\r\n\r\nThe main reason for this is linked to the reliability of AM manufactured parts. In aircraft engines for example even the small uncertainties associated to today more reliable methods can inherently affect the mechanical properties and life of final components. As example the manufacturing errors due to laser percussion drilling in High Pressure Turbine Nozzles can affect their life by more than 30%. If we manufacture the same component with AM this number is going to growth even further.\r\n\r\nDespite the fact that AM can revolutionize Aviation and critical applications in general, until we are able to quantify and minimize the impact of the variability associated to the process, it will be not possible to use such parts in risky applications.\r\n\r\nThe project addresses this aspect with the following key objectives:\r\n\r\n1)Quantify the impact of variability of the additive manufacturing process over a long period to account the impact of machine modifications\r\n\r\n2)Estimate the impact of boundary conditions uncertainties (powder, laser power etc) on the final product\r\n\r\n3)Propagate these uncertainties via a multiscale-Multiphysics model developed by ESI-group\r\n\r\n4)Validate the results with experimental validations\r\n\r\nThe project is predicated upon use a virtual environment (the so called digital twin) for the AM process that accounts the random variations of the process. There are several advantages associated to this:\r\n\r\n1)Understanding of the driving factors affecting the variability of AM components\r\n\r\n2)Estimate the reliability of AM parts, their residual stresses with virtual modelling\r\n\r\n3)Replace the need to do extensive experimental testing in order to quantify the variability of AM parts\r\n\r\n4)Minimize the impact of such variability to use AM parts in critical components.\r\n\r\nThere are several challenging aspects linked to this activity:\r\n\r\n1)The physical model of AM itself is involving several models.\r\n\r\n2) the UQ modelneed to be able to combine multiple data sets (scarce data, non Askey scheme distributions, ) across a complex problem and needs to be able to quantify low probability events. A major limitation of Additive Manufacturing is linked to the reliability of the produce critical components: the variability of the end products is limiting the range of possible applications, and the components are not used for critical applications.\r\n\r\nIt has been shown lately by Air Force Research Laboratory in US (ASME 2017 June) that all parameters of AM components are showing an unprecedented level of uncertainty for aircraft components, for example in their bending resistance.\r\n\r\nThe idea is that this uncertainty is due to the process variations, powder properties, geometrical uncertainty etc.\r\n\r\nIt is not possible to use experiments to fully characterize these variations, in part because in some applications only a limited number of products are needed and in part because of the associated cost of such experiments.\r\n\r\nThe aim of this proposal is to broaden innovation in additive manufacturing by introducing Uncertainty Quantification for Additive Manufacturing in order to assess and minimize the impact of these variations in AM components.\r\n\r\nThis is a very active area in US and UK is lacking behind. The overall goal of this proposal is to address this and to allow Additive Manufacturing to be used for critical components, such as Aircraft Engines, Medical Components or Cars.\r\n\r\nThere is a strong interest from industry on this as shown by MBDA call for UQ for Additive Manufacturing or KTN Special Interest Group in AM but the problem needs to be tackled and the results disseminated to UK Industry.\r\n\r\nThe project focuses on the following key objectives:\r\n\r\n1)Quantify the impact of variability of the additive manufacturing process over a long period to account the impact of machine modifications\r\n\r\n2)Estimate the impact of boundary conditions uncertainties (powder, laser power etc) on the final product\r\n\r\n3)Propagate these uncertainties via a multiscale-Multiphysics model developed by ESI-group\r\n\r\n4)Extend the applicability of AM to critical components. Additive Manufacturing is a disruptive technology that is moving from a rapid prototyping method to a more standard manufacturing process to build metal structures. In particular there is a strong interest in aviation and a key factor is the usage of AM parts in critical applications.\r\n\r\nIn order to use such parts in aviation an extensive experimental validation is needed and these components are not used for critical parts. This is due to the fact that the AM process is affected by many uncertainties, more than any other more standard process. The result is a strong variability in the reliability of the produced parts.\r\n\r\nThe majority of dynamic and static properties of AM components are affected by uncertainty. From the bending fatigue to the residual stresses, the scatter in the data is much higher to what found with more standard manufacturing methods.\r\n\r\nThis project will leverage the advanced computational model and data analytics in order to address this aspect. In particular a digital twin of the AM process under uncertainty will be developed and verified with experimental tests.\r\n\r\nWith this project UK will be able to be at the forefront of AM prediction capabilities with a strong impact on the all chain of industry 4.0 developed.", 
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