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Laser power based surface characteristics models for 3-D printing process

Author

Listed:
  • A. Garg

    (Nanyang Technological University)

  • Jasmine Siu Lee Lam

    (Nanyang Technological University)

  • M. M. Savalani

    (The Hong Kong Polytechnic University)

Abstract

Selective laser melting (SLM) is one of the important 3-D Printing processes that builds components of complex 3D shapes directly from the metal powder. It is widely used in manufacturing industries and is operated on significant amount of laser power drawn from the electric grid. The literature reveals that the properties such as surface roughness, waviness, tensile strength and dimensional accuracy of an SLM fabricated parts, depend on the laser power and can be improved by its appropriate adjustment. Determination of accurate values of laser power and the other inputs could lead to an improvement in energy efficiency and thus contributing to a clean and healthy environment. For determining the accurate value of laser power in achieving the required surface characteristics, the formulation of generalized mathematical models is an essential pre-requisite. In this context, an artificial intelligence approach of multi-gene genetic programming (MGGP) which develops the functional expressions between the process parameters automatically can be applied. The present work introduces an ensemble-based-MGGP approach to model the SLM process. Experiments on the SLM process with measurement of surface characteristics, namely surface roughness and waviness, based on the variations of laser power and other inputs are conducted, and the proposed ensemble-based-MGGP approach is applied. Statistical evaluation concludes that the performance of the proposed approach is better than that of the standardized MGGP approach. Sensitivity and parametric analysis conducted reveals the hidden relationships between surface characteristics and the laser power, which can be used to optimize the SLM process both economically and environmentally.

Suggested Citation

  • A. Garg & Jasmine Siu Lee Lam & M. M. Savalani, 2018. "Laser power based surface characteristics models for 3-D printing process," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1191-1202, August.
  • Handle: RePEc:spr:joinma:v:29:y:2018:i:6:d:10.1007_s10845-015-1167-9
    DOI: 10.1007/s10845-015-1167-9
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    Citations

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    Cited by:

    1. Hong Seok Park & Dinh Son Nguyen & Thai Le-Hong & Xuan Tran, 2022. "Machine learning-based optimization of process parameters in selective laser melting for biomedical applications," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1843-1858, August.
    2. Hongjian Wu & Shaowu Liu & Xinliang Xie & Yicha Zhang & Hanlin Liao & Sihao Deng, 2022. "A framework for a knowledge based cold spray repairing system," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1639-1647, August.
    3. Thai Le-Hong & Pai Chen Lin & Jian-Zhong Chen & Thinh Duc Quy Pham & Xuan Tran, 2023. "Data-driven models for predictions of geometric characteristics of bead fabricated by selective laser melting," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1241-1257, March.
    4. 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.

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