Model decomposition method for minimizing the consumption of support structure for FFF

被引:0
|
作者
Wei, Wei [1 ,3 ]
Wu, Haixin [1 ,3 ]
Zhang, Jiangzhao [1 ,3 ]
Zhang, Mingtao [1 ,3 ]
Yuan, Lili [2 ]
Zhou, Zhukun [1 ,3 ]
Long, Yu [1 ,3 ]
机构
[1] Guangxi Univ, Inst Laser Intelligent Mfg & Precis Proc, Sch Mech Engn, Nanning 530004, Guangxi, Peoples R China
[2] Nanning Univ, Guangxi Key Lab Int Join China ASEAN Comprehens Tr, Nanning 530000, Guangxi, Peoples R China
[3] Guangxi Univ, State Key Lab Featured Met Mat & Life cycle Safety, Nanning 530004, Guangxi, Peoples R China
关键词
FFF; 3D printing; Support-free; Multi-axis; Model decomposition method; TOPOLOGY OPTIMIZATION; DESIGN OPTIMIZATION; TITANIUM-ALLOYS;
D O I
10.1016/j.jmapro.2024.11.068
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Fused filament fabrication (FFF) often requires additional support structures for manufacturing parts with overhanging areas, leading to substantial material and time wastage. Although several model decomposition techniques have been developed to tackle this issue, most focus solely on the constraint of overhang surface area. As a result, these techniques often struggle with parts that have intricate overhang characteristics, causing reduced printing surface accuracy and quality. This study introduces a precise method for detecting overhang regions and support-relevant overhang regions to improve the accuracy of overhang region identification and printing precision. Additionally, a model decomposition strategy leveraging genetic algorithm optimization is proposed to minimize the need for support structures, aiming for nearly support-free printing. Moreover, for intricate structures with a high topology where support structures are unavoidable, a novel projection support based on the distribution of periodic points in triangular form is developed to address this printing challenge. Experimental results demonstrate that the implementation of the algorithm outlined in this work on an FFF printer has achieved nearly support-free printing. Moreover, the proposed method for generating support structures has the potential to reduce material usage by 27 %.
引用
收藏
页码:395 / 407
页数:13
相关论文
共 50 条
  • [21] Decomposition Method for Changes in the Structure
    Markowska, Malgorzata
    Sokolowski, Andrzej
    PRACE KOMISJI GEOGRAFII PRZEMYSLU POLSKIEGO TOWARZYSTWA GEOGRAFICZNEGO-STUDIES OF THE INDUSTRIAL GEOGRAPHY COMMISSION OF THE POLISH GEOGRAPHICAL SOCIETY, 2016, 30 (03): : 25 - 32
  • [22] A simple decomposition method for support vector machines
    Hsu, CW
    Lin, CJ
    MACHINE LEARNING, 2002, 46 (1-3) : 291 - 314
  • [23] On the convergence of the decomposition method for support vector machines
    Lin, CJ
    IEEE TRANSACTIONS ON NEURAL NETWORKS, 2001, 12 (06): : 1288 - 1298
  • [24] A Simple Decomposition Method for Support Vector Machines
    Chih-Wei Hsu
    Chih-Jen Lin
    Machine Learning, 2002, 46 : 291 - 314
  • [25] Improved decomposition method for support vector machines
    Zhou, WD
    Zhang, L
    Jiao, LC
    ICCIMA 2003: FIFTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND MULTIMEDIA APPLICATIONS, PROCEEDINGS, 2003, : 32 - 36
  • [26] Coordinated Caching Model for Minimizing Energy Consumption in Radio Access Network
    Xu, Yuemei
    Li, Yang
    Wang, Zihou
    Lin, Tao
    Zhang, Guoqiang
    Ci, Song
    2014 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2014, : 2406 - 2411
  • [27] Thermal model of precast concrete curing process: Minimizing energy consumption
    Mostafavi, Seyed Alireza
    Joneidi, Zahra
    MATHEMATICS AND COMPUTERS IN SIMULATION, 2022, 191 : 82 - 94
  • [28] DECOMPOSITION OF INDUSTRIAL ENERGY-CONSUMPTION - AN ALTERNATIVE METHOD
    PARK, SH
    ENERGY ECONOMICS, 1992, 14 (04) : 265 - 270
  • [29] DUAL DECOMPOSITION METHOD FOR MINIMIZING TRANSPORTATION COSTS IN MULTIFACILITY LOCATION PROBLEMS.
    Love, Robert F.
    Kraemer, Svend A.
    Transportation Science, 1973, 7 (04) : 297 - 316
  • [30] A method for minimizing the energy consumption of machining system: integration of process planning and scheduling
    Zhang, Zhongwei
    Tang, Renzhong
    Peng, Tao
    Tao, Liyan
    Jia, Shun
    JOURNAL OF CLEANER PRODUCTION, 2016, 137 : 1647 - 1662