Rule-Based Path Identification for Direct Energy Deposition

被引:5
|
作者
Sefidi, Moein Pakdel [1 ]
Israr, Rameez [1 ]
Buhl, Johannes [1 ]
Bambach, Markus [1 ]
机构
[1] Brandenburg Tech Univ Cottbus, Chair Mech Design & Mfg, Konrad Wachsmann Allee 17, D-03046 Cottbus, Germany
关键词
Rule-Based Path Identification; RBPI; AM optimization; WAAM; DED; WIRE; BEHAVIOR; ALLOY; VALIDATION; MODEL;
D O I
10.1016/j.promfg.2020.04.133
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Additive Manufacturing (AM) provides the possibility to produce complex part geometries on a layer-by-layer basis. In AM processes, a heat source moves over the part to either deposit powder or wire feedstock or to melt thin powder layers that are spread onto a powder bed. The moving heat source inevitably creates an inhomogeneous temperature distribution, which affects the residual stresses, part distortion, and local mechanical properties. Accumulation of heat in corners and path intersections may result in overheating and hence defects. In this study, a method for improving the temperature distribution in direct energy deposition processes is presented. FEM simulations in LS Dyna are coupled to MATLAB in order to divide a basic AM-FEM model and to adjust the model rule-based during the calculation. With this Rule-Based Path Identification (RBPI), the temperature history is used to choose the position of the next bead. With one bead wide wall, a temperature improvement of 90 degrees C could be achieved. The new simulation method is adopted for a delay time between the beads for 10s. In conclusion, it is shown that the RBPD helps to reduce the inhomogeneous temperature distribution for a metal printing process without expensive optimization. (c) 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 23rd International Conference on Material Forming.
引用
收藏
页码:1134 / 1140
页数:7
相关论文
共 50 条
  • [21] A method for structure identification in complete rule-based fuzzy systems
    Pomares, H
    Rojas, I
    González, J
    Prieto, A
    10TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3: MEETING THE GRAND CHALLENGE: MACHINES THAT SERVE PEOPLE, 2001, : 376 - 379
  • [22] RULE-BASED COMPETITION
    SCHARPING, R
    FOREIGN AFFAIRS, 1994, 73 (04) : 192 - 194
  • [23] Rule-based languages
    Victor Vianu
    Annals of Mathematics and Artificial Intelligence, 1997, 19 : 215 - 259
  • [24] Rule-based XML
    Eguchi G.
    Leff L.L.
    Artificial Intelligence and Law, 2002, 10 (4) : 283 - 294
  • [25] RULE-BASED SYSTEMS
    HAYESROTH, F
    COMMUNICATIONS OF THE ACM, 1985, 28 (09) : 921 - 932
  • [26] Rule-based languages
    Vianu, V
    ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE, 1997, 19 (1-2) : 215 - 259
  • [27] RULE-BASED INDUCTION
    BUSCH, H
    FORMAL METHODS IN SYSTEM DESIGN, 1994, 5 (1-2) : 7 - 33
  • [28] RULE-BASED PROGRAMMING
    MOSKOWITZ, L
    BYTE, 1986, 11 (12): : 217 - &
  • [29] Fuzzy rule-based models for home energy consumption prediction
    Nie, Peng
    Roccotelli, Michele
    Fanti, Maria Pia
    Li, Zhiwu
    ENERGY REPORTS, 2022, 8 : 9279 - 9289
  • [30] Rule-Based Fiscal Policy: The Case Of Rule-Based Fiscal Policy In Turkey
    Karakurt, Birol
    Akdemir, Tekin
    MALIYE DERGISI, 2010, (158): : 226 - 261