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All issues Volume 231 (2021) E3S Web Conf., 231 (2021) 03003 References
Open Access
Issue
E3S Web Conf.
Volume 231, 2021
2020 2nd International Conference on Power, Energy and Electrical Engineering (PEEE 2020)
Article Number 03003
Number of page(s) 4
Section Mechanical and Manufacturing Engineering
DOI https://doi.org/10.1051/e3sconf/202123103003
Published online 25 January 2021
  1. Liu J. Gaynor AT, Chen S, et al (2018) Current and future trends in topology optimization for additive manufacturing. Struct Multidiscip Optim 57: 2457–2483 . doi: 10.1007/s00158-018-1994-3 [CrossRef] [Google Scholar]
  2. Jiang J, Stringer J, Xu X. Zheng P (2018) A benchmarking part for evaluating and comparing support structures of additive manufacturing. In: 3rd International Conference on Progress in Additive Manufacturing (Pro-AM 2018). pp 196–202 [Google Scholar]
  3. Fu Y-F, Rolfe B, Chiu LNS, et al (2019) Design and experimental validation of self-supporting topologies for additive manufacturing. Virtual Phys Prototyp 14:382–394 . doi: 10.1080/17452759.2019.1637023 [CrossRef] [Google Scholar]
  4. Jiang J, Xu X. Jonathan Stringer (2019) Effect of Extrusion Temperature on Printable Threshold Overhang in Additive Manufacturing. In: CIRP Manufacturing Systems Conference 2019. Ljubljana [Google Scholar]
  5. Jiang J, Stringer J, Xu X (2019) Support Optimization for Flat Features via Path Planning in Additive Manufacturing. 3D Print Addit Manuf 6:171–179 . doi: 10.1089/3dp.2017.0124 [CrossRef] [Google Scholar]
  6. Jiang J, Xu X, Stringer J (2018) Support Structures for Additive Manufacturing: A Review. J Manuf Mater Process 2:64 . doi: 10.3390/jmmp2040064 [Google Scholar]
  7. Jiang J (2020) A novel fabrication strategy for additive manufacturing processes. J Clean Prod 272:122916 . doi: 10.1016/j.jclepro.2020.122916 [CrossRef] [Google Scholar]
  8. Jiang J, Xu X, Xiong Y, et al (2020) A novel strategy for multi-part production in additive manufacturing. Int J Adv Manuf Technol 109:1237–1248 . doi: 10.1007/s00170-020-05734-8 [CrossRef] [Google Scholar]
  9. Jiang J, Ma Y (2020) Path planning strategies to optimize accuracy, quality, build time and material use in additive manufacturing: A review. Micromachines 11:633 [CrossRef] [Google Scholar]
  10. Jiang J, Yu C, Xu X, et al (2020) Achieving better connections between deposited lines in additive manufacturing via machine learning. Math Biosci Eng 17:3382–3394 [CrossRef] [PubMed] [Google Scholar]
  11. Yu C, Jiang J (2020) A Perspective on Using Machine Learning in 3D Bioprinting. Int J Bioprinting 6:4–11 . doi: 10.18063/ijb.v6i1.253 [Google Scholar]
  12. Tamburrino F, Graziosi S, Bordegoni M (2019) The influence of slicing parameters on the multi-material adhesion mechanisms of FDM printed parts: an exploratory study. Virtual Phys Prototyp. doi: 10.1080/17452759.2019.1607758 [Google Scholar]
  13. Jiang J, Stringer J, Xu X, Zhong RY (2018) Investigation of printable threshold overhang angle in extrusion-based additive manufacturing for reducing support waste. Int J Comput Integr Manuf 31:961–969 . doi: 10.1080/0951192X.2018.1466398 [CrossRef] [Google Scholar]
  14. Jiang J, Lou J, Hu G (2019) Effect of Support on Printed Properties in Fused Deposition Modelling Processes. Virtual Phys Prototyp 14:308–315 . doi: 10.1080/17452759.2019.1568835 [CrossRef] [Google Scholar]
  15. Jiang J, Weng F, Gao S, et al (2019) A Support Interface Method for Easy Part Removal in Direct Metal Deposition. Manuf Lett 20:30–33 . doi: 10.1016/j.mfglet.2019.04.002 [CrossRef] [Google Scholar]
  16. Jiang J, Hu G, Li X, et al (2019) Analysis and prediction of printable bridge length in fused deposition modelling based on back propagation neural network. Virtual Phys Prototyp 14:253–266 . doi: 10.1080/17452759.2019.1576010 [CrossRef] [Google Scholar]
  17. Weng F, Gao S, Jiang J, et al (2019) A novel strategy to fabricate thin 316L stainless steel rods by continuous direct metal deposition in Z direction. Addit Manuf 27:474–481 . doi: 10.1016/j.addma.2019.03.024 [Google Scholar]
  18. Jiang J, Xu X, Stringer J (2019) Optimisation of multi-part production in additive manufacturing for reducing support waste. Virtual Phys Prototyp 14:219–228 . doi: 10.1080/17452759.2019.1585555 [CrossRef] [Google Scholar]
  19. Jiang J, Xu X, Stringer J (2019) Optimization of Process Planning for Reducing Material Waste in Extrusion Based Additive Manufacturing. Robot Comput Integr Manuf 59:317–325 . doi: 10.1016/j.rcim.2019.05.007 [CrossRef] [Google Scholar]
  20. Jiang J, Xu X, Stringer J (2018) A new support strategy for reducing waste in additive manufacturing. In: The 48th International Conference on Computers and Industrial Engineering (CIE 48). Auckland, pp 1–7 [Google Scholar]
  21. Luu TH, Altenhofen C, Ewald T, et al (2019) Efficient slicing of Catmull–Clark solids for 3D printed objects with functionally graded material. Comput Graph. doi: 10.1016/j.cag.2019.05.023 [Google Scholar]
  22. Wang Y, Li W (2019) A slicing algorithm to guarantee non-negative error of additive manufactured parts. Int J Adv Manuf Technol. doi: 10.1007/s00170-018-3199-8 [Google Scholar]
  23. Flores J, Garmendia I, Pujana J (2019) Toolpath generation for the manufacture of metallic components by means of the laser metal deposition technique. Int J Adv Manuf Technol. doi: 10.1007/s00170-018-3124-1 [Google Scholar]
  24. Volpato N, Zanotto TT (2019) Analysis of deposition sequence in tool-path optimization for low-cost material extrusion additive manufacturing. Int J Adv Manuf Technol. doi: 10.1007/s00170-018-3108-1 [Google Scholar]
  25. Ezair B, Fuhrmann S, Elber G (2018) Volumetric covering print-paths for additive manufacturing of 3D models. CAD Comput Aided Des 100:1–13 . doi: 10.1016/j.cad.2018.02.006 [CrossRef] [Google Scholar]
  26. Xiong Y, Park SI, Padmanathan S, et al (2019) Process planning for adaptive contour parallel toolpath in additive manufacturing with variable bead width. Int J Adv Manuf Technol. doi: 10.1007/s00170-019-03954-1 [Google Scholar]

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