CAD-based design and pre-processing tools for additive manufacturing

被引:38
|
作者
Zhang, Botao [1 ]
Goel, Archak [1 ]
Ghalsasi, Omkar [1 ]
Anand, Sam [1 ]
机构
[1] Univ Cincinnati, Dept Mech & Mat Engn, Ctr Global Design & Mfg, Cincinnati, OH 45221 USA
关键词
Design for additive manufacturing; Producibility index; Support structures; Build orientation optimization; PBFAM processes; SUPPORT STRUCTURES;
D O I
10.1016/j.jmsy.2019.03.005
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper discusses a set of geometry based computational pre-processing algorithms developed for Powder Bed Fusion Additive Manufacturing (PBFAM) processes. To start with, based on an initial part design, an automatic support structure generation module generates customized CAD-based support structures for a given part build orientation. Various additive manufacturing (AM) parameters and Design for Additive Manufacturing (DFAM) metrics are calculated on the fly for assigning producibility scores at different part build orientations. A set of stand-alone computational geometry-based algorithms with associated graphical user interfaces (GUI) are developed for calculating support structure parameters, as well as for detecting and highlighting DFAM features that are difficult to manufacture. These stand-alone tools provide a quantified output for each of the parameters or features, which are then used downstream during the producibility index (PI) calculation. An algorithm that evaluates ease of removing supports during the post-processing phase, and suggests the optimum number of setups needed to remove support structures is developed. Finally, Producibility Index, which is a weighted optimization metric, brings together the quantified outputs of the DFAM analysis, support structure parameters, accessibility analysis and suggests the best build orientations for the given part geometry. All the algorithms are implemented within the Siemens NX modelling environment utilizing C+ + and NX API functions. The developed algorithm and tools have been succesfully demonstrated on two sample parts.
引用
收藏
页码:227 / 241
页数:15
相关论文
共 50 条
  • [41] CAD-based design parameterization for shape optimization of elastic solids
    Univ of Iowa, Iowa City, United States
    [J]. Adv Eng Software, 3 (185-199):
  • [42] Design parameterization and tool integration for CAD-based mechanism optimization
    Chang, Kuang-Hua
    Joo, Sung-Hwan
    [J]. ADVANCES IN ENGINEERING SOFTWARE, 2006, 37 (12) : 779 - 796
  • [43] Speaker recognition based on pre-processing approaches
    Abd El-Moneim, Samia
    El-Rabaie, El-Sayed Mahmoud
    Nassar, M. A.
    Dessouky, Moawad, I
    Ismail, Nabil A.
    El-Fishawy, Adel S.
    Abd El-Samie, Fathi E.
    [J]. INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY, 2020, 23 (02) : 435 - 442
  • [44] FPGA Based Filters for EEG Pre-processing
    Sundaram, Kalyana
    Marichamy
    Pradeepa
    [J]. 2016 SECOND INTERNATIONAL CONFERENCE ON SCIENCE TECHNOLOGY ENGINEERING AND MANAGEMENT (ICONSTEM), 2016, : 572 - 576
  • [45] Speaker recognition based on pre-processing approaches
    Samia Abd El-Moneim
    El-Sayed M. EL-Rabaie
    M. A. Nassar
    Moawad I. Dessouky
    Nabil A. Ismail
    Adel S. El-Fishawy
    Fathi E. Abd El-Samie
    [J]. International Journal of Speech Technology, 2020, 23 : 435 - 442
  • [46] An Engineering Evaluation on the Glimpse of Satellite Image Pre-processing Utility Tools
    Abburu, Sunitha
    Golla, Suresh Babu
    [J]. ENGINEERING JOURNAL-THAILAND, 2015, 19 (02): : 129 - 138
  • [47] Design and Processing of Gas Turbine Blades Based on Additive Manufacturing Technology
    Liu, Xuan
    Han, Xingguo
    Yin, Guofu
    Song, Xiaohui
    Cui, Lixiu
    [J]. MICROMACHINES, 2023, 14 (09)
  • [48] A pre-processing tool to increase performance of deep learning-based CAD in digital breast Tomosynthesis
    Daniele Esposito
    Gianfranco Paternò
    Roberta Ricciardi
    Antonio Sarno
    Paolo Russo
    Giovanni Mettivier
    [J]. Health and Technology, 2024, 14 : 81 - 91
  • [49] CAD-based data augmentation and transfer learning empowers part classification in manufacturing
    Patrick Ruediger-Flore
    Moritz Glatt
    Marco Hussong
    Jan C. Aurich
    [J]. The International Journal of Advanced Manufacturing Technology, 2023, 125 : 5605 - 5618
  • [50] CAD-based data augmentation and transfer learning empowers part classification in manufacturing
    Ruediger-Flore, Patrick
    Glatt, Moritz
    Hussong, Marco
    Aurich, Jan C.
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2023, 125 (11-12): : 5605 - 5618