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A hybrid PSO–BFO evolutionary algorithm for optimization of fused deposition modelling process parameters

Author

Listed:
  • Maraboina Raju

    (Dr. B. R. Ambedkar NIT Jalandhar)

  • Munish Kumar Gupta

    (NIT)

  • Neeraj Bhanot

    (Indian Institute of Management)

  • Vishal S. Sharma

    (Dr. B. R. Ambedkar NIT Jalandhar)

Abstract

Fused deposition modeling (FDM), a well known 3D printing technology is widely used in various sorts of industrial applications because of its ability to manufacture complex objects in the stipulated time. However, the proper selection of input process parameters in FDM is a tedious task that directly affects the part performance. Here, in this work, the research efforts have been made to optimize the FDM process parameters in order to find out the best parameter setting as per the mechanical and surface quality perspectives by using hybrid particle swarm and bacterial foraging optimization (PSO–BFO) evolutionary algorithm. Taguchi L18 orthogonal array was used for the development of acro-nitrile butadiene styrene based 3D components by considering layer thickness, support material, model interior and orientation as a process parameters. Further, the relationships among selected FDM process parameters and output responses such as hardness, flexural modulus, tensile strength and surface roughness were established by using linear multiple regression. Then, the effects of individual process parameters on selected response parameters were examined by signal to noise ratio plots. Finally, a multi-objective optimization of process parameters has been performed with hybrid PSO–BFO, general PSO and BFO algorithm, respectively. The overall results reveal that the layer thickness of 0.007 mm, support material type sparse, part orientation of 60 $${^\circ }$$ ∘ and model interior of high density helps in achieving desired performance level.

Suggested Citation

  • Maraboina Raju & Munish Kumar Gupta & Neeraj Bhanot & Vishal S. Sharma, 2019. "A hybrid PSO–BFO evolutionary algorithm for optimization of fused deposition modelling process parameters," Journal of Intelligent Manufacturing, Springer, vol. 30(7), pages 2743-2758, October.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:7:d:10.1007_s10845-018-1420-0
    DOI: 10.1007/s10845-018-1420-0
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    Cited by:

    1. Sanath Alahakoon & Rajib Baran Roy & Shantha Jayasinghe Arachchillage, 2023. "Optimizing Load Frequency Control in Standalone Marine Microgrids Using Meta-Heuristic Techniques," Energies, MDPI, vol. 16(13), pages 1-23, June.
    2. Raghav Prasad Parouha & Pooja Verma, 2022. "An innovative hybrid algorithm for bound-unconstrained optimization problems and applications," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1273-1336, June.
    3. Chunyang Xia & Zengxi Pan & Joseph Polden & Huijun Li & Yanling Xu & Shanben Chen, 2022. "Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1467-1482, June.

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