A slicing algorithm to guarantee non-negative error of additive manufactured parts

被引:4
|
作者
Wang, Yu [1 ]
Li, Weishi [1 ]
机构
[1] Hefei Univ Technol, Sch Instrument Sci & Optoelect Engn, Hefei 23009, Anhui, Peoples R China
来源
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2019年 / 101卷 / 9-12期
基金
中国国家自然科学基金;
关键词
Additive manufacturing; Slicing; Staircase effect; Non-negative error;
D O I
10.1007/s00170-018-3199-8
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Despite the potential benefits of additive manufacturing over traditional subtractive manufacturing, there are still several intrinsic problems that affect the accuracy and quality of the additive manufactured parts adversely, one of which is the undercut, i.e., the additive manufactured part cannot enclose the corresponding designed model fully, according to existing slicing methods. Therefore, the accuracy of the post-processed parts cannot be guaranteed as post processing is generally a process of subtractive manufacturing. In this paper, we propose a slicing algorithm to generate a new kind of layer contours. In this algorithm, not only the input model is intersected with a group of parallel slicing planes to get the layer contours but also the local geometry between two adjacent slicing planes of a layer is integrated into the final layer contours to prevent the undercut. Consequently, a non-negative error is guaranteed on the whole surface of the manufactured part in theory in order to ensure the final accuracy of the post-processed part. Several slicing results are given to demonstrate the validity of the proposed algorithm.
引用
收藏
页码:3157 / 3166
页数:10
相关论文
共 50 条
  • [21] A convergent non-negative deconvolution algorithm with Tikhonov regularization
    Teng, Yueyang
    Zhang, Yaonan
    Li, Hong
    Kang, Yan
    INVERSE PROBLEMS, 2015, 31 (03)
  • [22] A DIAGONALIZED NEWTON ALGORITHM FOR NON-NEGATIVE SPARSE CODING
    Van Hamme, Hugo
    2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2013, : 7299 - 7303
  • [23] A non-negative representation learning algorithm for selecting neighbors
    Li, Lili
    Lv, Jiancheng
    Yi, Zhang
    MACHINE LEARNING, 2016, 102 (02) : 133 - 153
  • [24] A non-negative representation learning algorithm for selecting neighbors
    Lili Li
    Jiancheng Lv
    Zhang Yi
    Machine Learning, 2016, 102 : 133 - 153
  • [26] Non-negative Pyramidal Neural Network for Parts-based Learning
    Ferro, Milla S. A.
    Fernandes, Bruno J. T.
    Bastos-Filho, Carmelo J. A.
    2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2017, : 1709 - 1716
  • [27] An improved non-negative matrix factorization algorithm based on genetic algorithm
    Zhou, Sheng
    Yu, Zhi
    Wang, Can
    PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ELECTRONIC TECHNOLOGY, 2015, 6 : 395 - 398
  • [28] Exploring a copula-based alternative to additive error models-for non-negative and autocorrelated time series in hydrology
    Wani, Omar
    Scheidegger, Andreas
    Cecinati, Francesca
    Espadas, Gabriel
    Rieckermann, Joerg
    JOURNAL OF HYDROLOGY, 2019, 575 : 1031 - 1040
  • [29] Uniform error bounds for a continuous approximation of non-negative random variables
    Sanguesa, Carmen
    BERNOULLI, 2010, 16 (02) : 561 - 584
  • [30] Upper bound of Bayesian generalization error in non-negative matrix factorization
    Hayashi, Naoki
    Watanabe, Sumio
    NEUROCOMPUTING, 2017, 266 : 21 - 28