Opening New Opportunities For Aeronautic, Naval And Train Large Components Realization With Hybrid And Twin Manufacturing
| 1 | Research Institute in Civil and Mechanical Engineering, Centrale Nantes, France |
| 2 | Additive Manufacturing Group, Joint Laboratory of Marine Technology (JLMT) Centrale Nantes – Naval Group, France |
CORRESPONDING AUTHOR
Submission date: 2022-06-01
Final revision date: 2022-10-14
Acceptance date: 2022-10-17
Online publication date: 2022-11-04
Publication date: 2022-12-22
Journal of Machine Engineering 2022;22(4):5–20
KEYWORDS
TOPICS
ABSTRACT
Additive Manufacturing proposes innovative directions for high value components and has benefitted from large research efforts for almost all existing industrial sectors. This paper introduces some opportunities and the associated challenges attached to Additive Manufacturing, to produce large metallic components for naval aeronautics and train industries. Two innovative approaches are discussed. Hybrid manufacturing consists in integrating AM together with other processes with the objective to benefit from the interests of each process while avoiding its drawbacks. Finding the optimal manufacturing work plan can be challenging. Twin manufacturing uses models and multiphysics simulation to create a digital clone of the process implementation within its environment. Various configurations and choices can be tested before being selected. The digital twin can also be fed by monitoring data captured during the process. The paper is illustrated with several proof-of-concept parts.
REFERENCES (19)
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.sie..., (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/..., (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.
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.
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.