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Development of Interdisciplinary Assessment for manufacturing and deSign

Periodic Reporting for period 1 - DIAS (Development of Interdisciplinary Assessment for manufacturing and deSign)

Reporting period: 2020-06-01 to 2021-08-31

DIAS develop and demonstrate the integrated multidisciplinary tools necessary to speed up implantation and design integration or Open Rotor structural components in next generation aircrafts. This require more flexible design studies,
allowing a wider set of design variants to be explored simultaneously with accounting for manufacturing equipment and process applicability. .
DIAS bring in state of the art modelling and simulation techniques for robot path assessment, geometrical variation of weld assembly using advanced modelling software and combines these with architectural modelling tools (CCM, CAM) that allow impact assessment on system level, risk and cost to be included in the same study. The studies are conduced as evolving and digital design of experiments first outlining the desired design space, identifying feasible regimes of the design space and generate variations of geometries and manufacturing technologies. The results from the simulations are used and compared with reference data a from Clean Sky high fidelity simulations and experiments to form validated surrogate models. Decision makers and specialist from multiple domains team up and conduct real time trade off analyses to identify most resilient design. AI and Machine learning algorithms are used to facilitate intelligent and interactive decision support.
The expected outcome is expected to allow the manufacturer to optimize new design and reduce the risk to introduce Clean Sky demonstrated configurations and technologies to be introduced in next generation products. As such, DIAS have a profound impact on realizing the significant potential demonstrated in Clean Sky projects.
DIAS was launched in June 2020 and had a Digital Kick Off meeting with the Topic Leader (GKN Aerospace). An automated digital simulation and experimentation framework has been developed, that allow a range of definition, simulation and post processing tools to address weld manufacturability of advanced jet engine structural components together with performance evaluation for range of varying design parameters. Fraunhofer FCC developed algorithms to enable weld path evaluation for candidate designs, Chalmers have generated architectural models Enhance Functions Means Models, and generative geometrical modes in CAD that support a wide range of design parameters for a typical turbine structure of an jet engine. Cambridge have developed interactive decision support for the results processing, including developing ability to take results from E FM studies, via Design Structure Matrices, into the Cambridge Advanced Modeller where further analysis, learning and visualisation of results are provided.
The ability to integrate directly welding manufacturability into a conceptual design loop, with up to 1000 alternative geometrical designs are assesses simultaneously together with mechanical, cost and weight evaluation advances state of the art and the first results have been published and demonstrated publicly at the ICED (International Conference on Engineering Design) in August 2021.
The first fully automatic system support for DIAS methodology has been validated together with the topic lead to validate functionality and initiate exploitation.
DIAS brings the multidisciplinary design optimisation capabilities beyond the state of art by integrating
optimisation algorithms within the visualisation environment of multi-dimensional data. In this way,
quick iterations can be performed in focused areas of the design space with target ranges in the objective
functions. DIAS will also consider manufacturability aspects as objective functions rather than as constraints
only. The iterative process allows to increase the maturity of the models, but also the validity of Machine
Learning models that support the trade-off analysis and decision-making process.
MDO studies are utilised within the design process; starting with low-fidelity models, or small number of
expensive and more accurate evaluations in a large design space where the risk is high, but identifying the
region of interest at each stage with the support of Machine Learning. The number of evaluations and the
fidelity of the models increases in the next iteration. Hence, the risk in managed in an effective way and richer
understanding can be reached in a given time frame.
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