Image-based truss recognition for density-based topology optimization approach

被引:18
|
作者
Gamache, Jean-Francois [1 ]
Vadean, Aurelian [1 ]
Noirot-Nerin, Emeric [2 ]
Beaini, Dominique [1 ]
Achiche, Sofiane [1 ]
机构
[1] Polytech Montreal, Machine Design Sect, Dept Mech Engn, Montreal, PQ, Canada
[2] Stelia Amer Nord, Res & Technol, Mirabel, PQ, Canada
关键词
Topology optimization; Aircraft design; Feature recognition; SHAPE OPTIMIZATION; DESIGN; GEOMETRY;
D O I
10.1007/s00158-018-2028-x
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Topology optimization is a tool that supports the creativity of structural-designers and is used in various industries, from automotive to aeronautics, to reduce design iterations towards obtaining the optimal structure layout. However, this tool requires both time and experience to interpret the results into manufacturable and reliable structure layout. To improve this aspect, an interpretation support tool is being developed by our research team based on industrial knowledge and axiomatic design principles. This design tool will be very useful for aircraft structure development, for instance, as it aims to help the structural designer in the conception of stiffened panels. The tool has been divided into three modules: feature recognition, feature analysis and design support. This paper presents the first of the three modules that identifies the trusses of the optimized topology in terms of truss recognition algorithms. The purpose of the truss recognition algorithm is to translate the densities of the element of the optimized topology (low-level abstraction) to a skeletal structure (high-level abstraction) that contains nodes and branches that describe the same topology as the optimized topology. It should also ensure that the structural skeleton retains connectivity with loads and boundary conditions. The information may then be used by the design support tool for analysis, comparison, decision-making, design and optimization purposes. To do so, a novel image-based method using a binary skeleton is proposed. For this work, we identified multiple limitations existing in similar solutions and we mitigated them. Therefore, a new skeletonization method is proposed, which is specifically designed for truss recognition in the optimized topology. The capabilities of the skeletonization method are demonstrated by comparing it with existing methods, and the truss recognition algorithm is used with a test case exhibiting the algorithm's capabilities on an airplane wing box rib.
引用
收藏
页码:2697 / 2709
页数:13
相关论文
共 50 条
  • [41] Sensitivity Analysis and Filtering of Machinable Parts Using Density-Based Topology Optimization
    Vadillo Morillas, Abraham
    Meneses Alonso, Jesus
    Bustos Caballero, Alejandro
    Castejon Sisamon, Cristina
    APPLIED SCIENCES-BASEL, 2024, 14 (14):
  • [42] Image-based fish recognition
    Saitoh, Takeshi
    Shibata, Toshiki
    Miyazono, Tsubasa
    PROCEEDINGS OF THE 2015 SEVENTH INTERNATIONAL CONFERENCE OF SOFT COMPUTING AND PATTERN RECOGNITION (SOCPAR 2015), 2015, : 260 - 263
  • [43] Dictionaries for Image-based Recognition
    Patel, Vishal M.
    Qiu, Qiang
    Chellappa, Rama
    2013 INFORMATION THEORY AND APPLICATIONS WORKSHOP (ITA), 2013,
  • [44] Correction to: Multiscale Topology Optimization Combining Density-Based Optimization and Lattice Enhancement for Additive Manufacturing
    Jae-Eun Kim
    Keun Park
    International Journal of Precision Engineering and Manufacturing-Green Technology, 2021, 8 : 1369 - 1369
  • [45] Deep density-based image clustering
    Ren, Yazhou
    Wang, Ni
    Li, Mingxia
    Xu, Zenglin
    KNOWLEDGE-BASED SYSTEMS, 2020, 197
  • [46] Mixed neighborhood topology cross decoded patterns for image-based face recognition
    Kas, M.
    El Merabet, Y.
    Ruichek, Y.
    Messoussi, R.
    EXPERT SYSTEMS WITH APPLICATIONS, 2018, 114 : 119 - 142
  • [47] Image-Based Relation Classification Approach for Table Structure Recognition
    Ichikawa, Koji
    DOCUMENT ANALYSIS AND RECOGNITION - ICDAR 2021, PT II, 2021, 12822 : 632 - 647
  • [48] An ensemble approach for still image-based human action recognition
    Avinandan Banerjee
    Sayantan Roy
    Rohit Kundu
    Pawan Kumar Singh
    Vikrant Bhateja
    Ram Sarkar
    Neural Computing and Applications, 2022, 34 : 19269 - 19282
  • [49] An ensemble approach for still image-based human action recognition
    Banerjee, Avinandan
    Roy, Sayantan
    Kundu, Rohit
    Singh, Pawan Kumar
    Bhateja, Vikrant
    Sarkar, Ram
    NEURAL COMPUTING & APPLICATIONS, 2022, 34 (21): : 19269 - 19282
  • [50] Multi-GPU acceleration of large-scale density-based topology optimization
    Herrero-Perez, David
    Martinez Castejon, Pedro J.
    ADVANCES IN ENGINEERING SOFTWARE, 2021, 157