Towards a multi-sensor monitoring methodology for AM metallic processes

被引:0
|
作者
A. Chabot
M. Rauch
J.-Y. Hascoët
机构
[1] Centrale Nantes / GeM,
[2] UMR CNRS 6183,undefined
[3] équipe PMM,undefined
[4] Joint Laboratory of Marine Technology (JLMT) Centrale Nantes – Naval Group,undefined
来源
Welding in the World | 2019年 / 63卷
关键词
Additive Manufacturing; Direct Energy Deposition; Monitoring; Closed-loop control;
D O I
暂无
中图分类号
学科分类号
摘要
Additive Manufacturing (AM) is a promising manufacturing technology as compared to subtractive processes, in terms of cost and freedom of manufacturing. Among the AM techniques, Direct Energy Deposition (DED) processes are dedicated to functional metallic parts manufacturing. The energy input can be provided either by laser or electric arc, and having its deposited material as wire or powder form. DED processes incur drawbacks from lack of reproducibility and important production losses, mainly because they are operated in open-loop. Consequently, process monitoring is investigated to control the manufacturing state in real-time and ensure acceptable final parts. Presently, lots of papers have designed single closed-loop controls for DED processes, controlling either thermal, geometrical, or material delivery aspects. Multi-sensors monitoring strategies are also increasingly proposed, as controlling only one criterion has shown some limitations. Nevertheless, the developed multi-sensor strategies still focused on one type of phenomenon—mainly geometry—and have been implemented for only one DED process. This paper presents a new methodology of multi-sensor and multi-physics monitoring dedicated to at least two DED processes. The first investigations focus on a coupling between thermal and geometrical control loops, considering global part’s temperature and layer height for thermal and geometrical aspects respectively. At the end of this paper, perspectives are given for closed-loop corrections according to the precited descriptors. These perspectives will be implemented in further works.
引用
收藏
页码:759 / 769
页数:10
相关论文
共 50 条
  • [1] Towards a multi-sensor monitoring methodology for AM metallic processes
    Chabot, A.
    Rauch, M.
    Hascoet, J. -Y.
    [J]. WELDING IN THE WORLD, 2019, 63 (03) : 759 - 769
  • [2] Gaussian Processes for Multi-Sensor Environmental Monitoring
    Erickson, Philip
    Cline, Michael
    Tirpankar, Nishith
    Henderson, Tom
    [J]. 2015 IEEE INTERNATIONAL CONFERENCE ON MULTISENSOR FUSION AND INTEGRATION FOR INTELLIGENT SYSTEMS (MFI), 2015, : 208 - 213
  • [3] Towards a Smart Multi-Sensor Ionizing Radiation Monitoring System
    Andjelkovic, Marko
    Chen, Junchao
    Syed, Rizwan Tariq
    Vargas, Fabian
    Ulbricht, Markus
    Krstic, Milos
    Ilic, Stefan
    Marjanovic, Milos
    Veljkovic, Sandra
    Mitrovic, Nikola
    Dankovic, Danijel
    Ristic, Goran
    Duane, Russell
    Vasovic, Nikola
    Jaksic, Aleksandar
    Palma, Alberto J.
    Lallena, Antonio M.
    Carvajal, Miguel A.
    [J]. 2023 26TH EUROMICRO CONFERENCE ON DIGITAL SYSTEM DESIGN, DSD 2023, 2023, : 286 - 293
  • [4] A reliable methodology for monitoring unstable slopes: the multi-platform and multi-sensor approach
    Castagnetti, Cristina
    Bertacchini, Eleonora
    Corsini, Alessandro
    Rivola, Riccardo
    [J]. EARTH RESOURCES AND ENVIRONMENTAL REMOTE SENSING/GIS APPLICATIONS V, 2014, 9245
  • [5] A silicon-based multi-sensor chip for monitoring of fermentation processes
    Baecker, M.
    Pouyeshman, S.
    Schnitzler, Th.
    Poghossian, A.
    Wagner, P.
    Biselli, M.
    Schoening, M. J.
    [J]. PHYSICA STATUS SOLIDI A-APPLICATIONS AND MATERIALS SCIENCE, 2011, 208 (06): : 1364 - 1369
  • [6] Multi-sensor driver drowsiness monitoring
    Boyraz, P.
    Acar, M.
    Kerr, D.
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, 2008, 222 (D11) : 2041 - 2062
  • [7] An adaptive SPC approach for multi-sensor fusion and monitoring of time-varying processes
    Grasso, M.
    Albertelli, P.
    Colosimo, B. M.
    [J]. EIGHTH CIRP CONFERENCE ON INTELLIGENT COMPUTATION IN MANUFACTURING ENGINEERING, 2013, 12 : 61 - 66
  • [8] Synchronous Multi-Sensor Monitoring for Additive Manufacturing
    Plotnikov, Y.
    Henkel, D.
    Burdick, J.
    Bourne, K.
    [J]. MATERIALS EVALUATION, 2020, 78 (02) : 193 - 202
  • [9] Design and Implementation of a Multi-Sensor Monitoring System
    Ye Jihua
    Liu Yan
    Nie Xiaoshi
    [J]. 10TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE & EDUCATION (ICCSE 2015), 2015, : 921 - 924
  • [10] Multi-sensor driver monitoring for drowsiness prediction
    Schwarz, Chris
    Gaspar, John
    Yousefian, Reza
    [J]. TRAFFIC INJURY PREVENTION, 2023, 24 : S100 - S104