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Build orientation optimization for multi-part production in additive manufacturing

Author

Listed:
  • Yicha Zhang

    (IRCCyN, Ecole Centrale de Nantes)

  • Alain Bernard

    (IRCCyN, Ecole Centrale de Nantes)

  • Ramy Harik

    (University of South Carolina)

  • K. P. Karunakaran

    (Indian Institute of Technology Bombay)

Abstract

Build orientation is one of the most important process planning tasks in additive manufacturing (AM) since it directly affects the part quality, build time, cost etc. Many researchers have investigated the orientation optimization problem and proposed numerous solutions. However, former researches only focused on how to find an optimal orientation for one part, but none of the solutions was provided to solve the orientation optimization problem of Multi-part production, where a group of parts in the same build vat or chamber should be optimally-orientated simultaneously. This paper introduces a two-step solution to solve the problem. At first, a feature based method is used to generate a set of finite optimal alternative orientations for each part within a given part group to guarantee each part’s individual build quality; then an improved genetic algorithm is applied to search for an optimal combination of part build orientations to minimize the total build time and cost at a global optimal level. A case study of orientating 16 parts simultaneously within a given build chamber is presented for demonstration.

Suggested Citation

  • Yicha Zhang & Alain Bernard & Ramy Harik & K. P. Karunakaran, 2017. "Build orientation optimization for multi-part production in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1393-1407, August.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:6:d:10.1007_s10845-015-1057-1
    DOI: 10.1007/s10845-015-1057-1
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    Citations

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    Cited by:

    1. Ivanna Baturynska & Kristian Martinsen, 2021. "Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 179-200, January.
    2. Yuchu Qin & Qunfen Qi & Paul J. Scott & Xiangqian Jiang, 2019. "Determination of optimal build orientation for additive manufacturing using Muirhead mean and prioritised average operators," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 3015-3034, December.
    3. Konstantinos S. Boulas & Georgios D. Dounias & Chrissoleon T. Papadopoulos, 2023. "A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 823-852, February.
    4. Zeqi Hu & Xunpeng Qin & Yifeng Li & Jiuxin Yuan & Qiang Wu, 2020. "Multi-bead overlapping model with varying cross-section profile for robotic GMAW-based additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1133-1147, June.
    5. Yizhe Yang & Bingshan Liu & Haochen Li & Xin Li & Xiaodong Liu & Gong Wang, 2023. "Automatic selection system of the building orientation based on double-layer priority aggregation multi-attribute decision-making," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2477-2493, June.
    6. Abdullah Alfaify & Mustafa Saleh & Fawaz M. Abdullah & Abdulrahman M. Al-Ahmari, 2020. "Design for Additive Manufacturing: A Systematic Review," Sustainability, MDPI, vol. 12(19), pages 1-22, September.
    7. Mouhamadou Mansour Mbow & Christelle Grandvallet & Frederic Vignat & Philippe Rene Marin & Nicolas Perry & Franck Pourroy, 2022. "Mathematization of experts knowledge: example of part orientation in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1209-1227, June.
    8. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
    9. Jose M. Framinan & Paz Perez-Gonzalez & Victor Fernandez-Viagas, 2023. "An overview on the use of operations research in additive manufacturing," Annals of Operations Research, Springer, vol. 322(1), pages 5-40, March.
    10. Fuentes-Cortés, Luis Fabián & Flores-Tlacuahuac, Antonio, 2018. "Integration of distributed generation technologies on sustainable buildings," Applied Energy, Elsevier, vol. 224(C), pages 582-601.

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