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

  • Maraboina Raju
  • Munish Kumar Gupta
  • Neeraj Bhanot
  • Vishal S. Sharma
Article

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.

Keywords

Evolutionary algorithm Mechanical properties Optimization Surface roughness Rapid prototyping 

Abbreviations

FDM

Fused deposition modeling

BFO

Bacterial foraging optimization

S/N

Signal to noise

FM

Flexural modulus

Ra

Surface roughness

MI

Model interior

SM

Support material

n

No. of bacteria in population

Nr

No. of reproduction steps

Ns

No. of swim

Pbest

Particle best position (PSO)

\(w_{max}\)

Maximum inertia weight (PSO)

\(iter_{curr}\)

Current iteration (PSO)

c(I)

Length of unit walk (BFO)

\(Jcc \left( \theta ,P\left( {j,k,l} \right) \right) \)

Cost function value (BFO)

PSO

Particle swarm optimization

ABS

Acro-nitrile butadiene styrene

H

Hardness

TS

Tensile strength

LT

Layer thickness

PO

Part orientation

AM

Additive manufacturing

Ned

No. of elimination–dispersion

Nc

No. of chemo-tactic steps

Pcd

Dispersion probability

Gbest

Global best position (PSO)

\(w_{min} \)

Minimum inertia weight (PSO)

\(iter_{total}\)

Total number of iteration (PSO)

\(\phi \left( i \right) \)

Direction angle of the jth step (BFO)

Notes

Acknowledgements

The authors acknowledge the “Institute for Auto Parts and Hand Tools Technology, Ludhiana” and “Central Institute of Plastics Engineering and Technology, Amritsar” for helping in carrying out the experiments.

Author Contributions

The contributions of all authors are equivalent in this paper.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Maraboina Raju
    • 1
  • Munish Kumar Gupta
    • 2
  • Neeraj Bhanot
    • 3
  • Vishal S. Sharma
    • 1
  1. 1.I & P DepartmentDr. B. R. Ambedkar NIT JalandharJalandharIndia
  2. 2.MEDNITHamirpurIndia
  3. 3.Department of Quantitative methods and Operation ManagementIndian Institute of ManagementAmritsarIndia

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