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Opening new opportunities for aeronautic, naval and train large components realization with hybrid and twin manufacturing

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Additive Manufacturing (AM) consist in producing parts by depositing material in successive layers. These step-by-step processes proposes new innovative directions for high value components: complex geometries are accessible without strong efforts (such as hollow or lattice structures which dramatically reduce the component weight while keeping their at least similar mechanical properties), assemblies can be simplified, spare parts can be realized at demand... Hence, AM has benefitted from large research efforts over the last decade, almost all existing industrial sectors have benefitted from them. This paper introduces some opportunities and the associated challenges attached to Additive Manufacturing, to produce large metallic components for naval aeronautics and train industries. In particular, two innovative approaches are discussed in details: hybrid manufacturing and twin manufacturing. Hybrid manufacturing consists in integrating AM together with other processes for the realization of components, with the objective to benefit from the interests of each process while avoiding its drawbacks. Hence, AM can realize complex geometries or offer low buy-to-fly ratios while high speed machining generates very good surface properties (position, roughness). Processes can be carried out sequentially or simultaneously on the features to manufacture and finding the optimal manufacturing work plan can be challenging. The paper introduces some hybrid approaches developed in the laboratory. Twin manufacturing uses models and multiphysics simulation methods to create a digital clone of the process implementation within the manufacturing environment. Manufacturing preparation and optimization can be carried in the virtual workshop where various configurations and choices can be tested before being selected. To enhance its accuracy, the digital twin can also be fed by monitoring data captured during the process. Several digital twins developed in the laboratory are provided. The paper is illustrated with several proof-of-concept parts made with SLM, LMD, WAAM and hybrid approaches in the laboratory. Among them, a hollow propellers that has the same hydrodynamics efficiency for a reduced weight for the naval industry, an aircraft structural panel that demonstrates simplified assemblies increased performance/mass ratio, a train component that shows the ability to produce structural parts at demand.
Rocznik
Strony
5--20
Opis fizyczny
Bibliogr. 19 poz., rys.
Twórcy
  • Centrale Nantes / GeM – UMR CNRS, Nantes, France
  • Joint Laboratory of Marine Technology (JLMT) Centrale Nantes – Naval Group, Nantes, France
  • Centrale Nantes / GeM – UMR CNRS, Nantes, France
  • Joint Laboratory of Marine Technology (JLMT) Centrale Nantes – Naval Group, Nantes, France
Bibliografia
  • [1] ISO/ASTM 52900:2015, Fabrication additive – Principes generaux – Terminologie, https://www.iso.org/cms/render/live/fr/sites/isoorg/contents/data/standard/06/96/69669.html, (accessed Mar. 18, 2022).
  • [2] PONCHE R., KERBRAT O., MOGNOL P., HASCOET J.-Y., 2014, A Novel Methodology of Design for Additive Manufacturing Applied to Additive Laser Manufacturing Process, Robot. Comput.-Integr. Manuf., 30/4, 389–398, doi: 10.1016/j.rcim.2013.12.001.
  • [3] MULLER P., HASCOET J.-Y., MOGNOL P., 2014, Toolpaths for Additive Manufacturing of Functionally Graded Materials (FGM) Parts, Rapid Prototyp. J., 20/6, 511–522, doi: 10.1108/RPJ-01-2013-0011.
  • [4] RIVETTE M., HASCOET J.-Y., MOGNOL P., 2007, A Graph-Based Methodology for Hybrid Rapid Design, Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., 221/4, 685–697.
  • [5] MOGNOL P., RIVETTE M., JEGOU L., LESPRIER T., 2007, A First Approach to Choose Between HSM, EDM and DMLS Processes in Hybrid Rapid Tooling, Rapid Prototyp. J., 13/1, 7–16, doi: 10.1108/13552540710719163.
  • [6] NX, Siemens Digital Industries Software, https://www.plm.automation.siemens.com/global/fr/products/nx/, (accessed May 16, 2022).
  • [7] PowerMill: Expert CAM Software for high-speed and 5-Axis CNC Machining, Autodesk, https://www.autodesk.com/products/powermill/overview, (accessed May 16, 2022).
  • [8] ISO 14649-1, 2003, Industrial Automation Systems and Integration – Physical Device Control – Data Model for Computerized Numerical Controllers – Part 1: Overview and Fundamental Principles.
  • [9] ISO 6983-1, 2009, Automation Systems and Integration – Numerical Control of Machines – Program Format and Definitions of Address Words – Part 1, Data Format for Positioning, Line Motion and Contouring Control Systems, http://www.iso.org/cms/render/live/fr/sites/isoorg/contents/data/standard/03/46/3460, (accessed Feb. 15, 2022).
  • [10] RAUCH M., LAGUIONIE R., J HASCOET J.-Y., SUH S.-H., 2012, An Advanced STEP-NC Controller for Intelligent Machining Processes, Robot. Comput.-Integr. Manuf., 28/3, 375–384.
  • [11] HASCOET J.-Y., TOUZE S., RAUCH M., 2018, Automated Identification of Defect Geometry for Metallic Part Repair by an Additive Manufacturing Process, Weld. World, 62/2, 229–241, doi: 10.1007/s40194-017-0523-0.
  • [12] MERY D., 2006, Automated Radioscopic Inspection of Aluminium Die Castings, Mater. Eval., 65/6, 643–647.
  • [13] TABERNERO I., CALLEJA A., LAMIKIZ A., LOPEZ De LACALLE L.N., 2013, Optimal Parameters for 5-axis Laser Cladding, Procedia Eng., 63, 45–52, doi: 10.1016/j.proeng.2013.08.229.
  • [14] ALCACER V., CRUZ-MACHADO V., 2019, Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems, Eng. Sci. Technol. Int. J., Jan., doi: 10.1016/j.jestch.2019.01.006.
  • [15] RAUCH M,. HASCOET J.-Y., 2011, Interest of Multiphysics and Multilevel Simulation Approaches to Enhance the Machining Process, Advanced Materials Research, 223, 891–899.
  • [16] Ramsses Project: https://www.ramsses-project.eu/, (accessed May 18, 2020).
  • [17] MULLER P., RÜCKERT G., VINOT P., 2019, On the Benefits of Metallic Additive Manufacturing for Propellers, Sixth International Symposium on Marine Propulsors smp’19, Rome (Italy), p. 8.
  • [18] RYCKELYNCK D., CHINESTA F., CUETO E., AMMAR A., 2006, On the a Priori Model Reduction: Overview and Recent Developments, Arch. Comput. Methods Eng., 13/1, 91–128, doi: 10.1007/BF02905932.
  • [19] POULHAON F., LEYGUE A., RAUCH M., HASCOET J.-Y., CHINESTA F., 2014, Simulation-Based Adaptative Toolpath Generation in Milling Processes, Int. J. Mach. Mach. Mater., 15/3–4, 263–284.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-cc1147d9-5a8c-4262-8845-eb89739470c8
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